Automated vehicles need to estimate tire-road friction information, as it plays a key role in safe trajectory planning and vehicle dynamics control. Notably, friction is not solely dependent on road surface conditions, but also varies significantly depending on the tire temperature. However, tire parameters such as the friction coefficient have been conventionally treated as constant values in automated vehicle motion planning. This paper develops a simple thermodynamic model that captures tire friction temperature variation. To verify the model, it is implemented into trajectory planning for automated drifting - a challenging application that requires leveraging an unstable, drifting equilibrium at the friction limits. The proposed method which captures the hidden tire dynamics provides a dynamically feasible trajectory, leading to more precise tracking during experiments with an LQR (Linear Quadratic Regulator) controller.

This short letter proposes a ground-penetrating radar (GPR) measurement technique to better detect vertical fault that occurs in a base rock under a surface layer. The technique exploits polarization-dependent radar response of the linear feature of the fault end that appears on the featureless base rock surface. A set of GPR measurements of which polarizations are orthogonal to each other are carried out in a fashion that one polarization is parallel to the putative target fault line, while another is normal to it. Taking the difference of instantaneous power A-scope data of the measurements cancels contribution from the featureless base rock surface while the contribution from the fault fracture end is elicited, thanks to its strong polarization-dependent radar response. The technique was described by an analytic model, its feasibility was demonstrated by finite difference time domain (FDTD) simulation, and was verified by a field measurement experiment.

This article intends to establish a reasonable determination criterion for nadir detection of lunar lava tube by Kaguya Lunar Radar Sounder observation. We applied radar cross section analysis to a simple lava tube model that comprises vacuum/basalt media with flat media boundaries, which illustrated that radar echoes from a lava tube are continuously detected through a number of consecutive observations along orbit if the lava tube returns radar echoes strong enough. “Consecutive detection” makes the minimal determination criterion for the detection of a lava tube. We further investigated influence of curved ceiling of a lava tube on its detection using 2-D finite difference time domain (2-D FD-TD) simulation and radar cross section analysis with Geometrical Optics. The work revealed that basalt roof of a lava tube plays a role of a concave lens in the propagation process of a radar pulse hence contributes to reduce lava tube echo intensity in comparison to that of the simple lava tube model. The curved ceiling, however, does not affect the above derived determination criterion. Ambiguity test plays the crucial role in the detection of a lava tube by distinguishing surface clutter. We demonstrated three types of ambiguity tests, i.e., 1) multiorbit data analysis test, 2) surface scattering simulation test, and 3) surface slope echo analysis test. The last two tests were demonstrated on those data which had been documented as radar echoes from large scale lava tubes in Marius Hills in a recent literature. The test results indicated that they are nothing more than a collection of surface clutter.

This paper proposes a novel framework of training method of deep Gaussian processes (DGPs). DGPs are deep architecture models based on stacked multiple GPs, which can overcome the limitation of single-layer GPs. Although a stochastic variational inference (SVI)- based method has been proposed for DGP training with an arbitrary amount of training data, it does not always update model parameters appropriately due to repeated Monte Carlo sampling of multiple GPs. To resolve this problem, we propose a pretraining method to determine initial parameters of DGPs. In the proposed method, layer-wise training of single-layer GPs is performed using the hidden-layer values obtained by a deep neural network (DNN) that has an analogous structure to a target DGP model. The proposed method utilizes the characteristics of single-layer GPs and deep neural network (DNN) whose training is easier than DGP's. Experimental results using two speech synthesis databases with approximately 600 K and 1.4 M training data points, respectively, which were composed of hundreds of input and output features, gave the effectiveness of the proposed method.

This paper proposes a framework of speech synthesis based on deep Gaussian processes (DGPs), which is a deep architecture model composed of stacked Bayesian kernel regressions. In this method, we train a statistical model of transformation from contextual features to speech parameters in a similar manner to deep neural network (DNN)-based speech synthesis. To apply DGPs to a statistical parametric speech synthesis framework, our framework uses an approximation method, doubly stochastic variational inference, which is suitable for an arbitrary amount of data. Since the training of DGPs is based on the marginal likelihood that takes into account not only data fitting, but also model complexity, DGPs are less vulnerable to overfitting compared with DNNs. In experimental evaluations, we investigated a performance comparison of the proposed DGP-based framework with a feedforward DNN-based one. Subjective and objective evaluation results showed that our DGP framework yielded a higher mean opinion score and lower acoustic feature distortions than the conventional framework.

HF (5MHz) imaging of lunar surface was attempted using off nadir echo data of Kaguya Lunar Radar Sounder (LRS). LRS observation data of multiple orbits were processed and mapped onto a surface which was defined based on the Kaguya Digital Elevation Model. The transmitting/receiving antenna of LRS is a dipole antenna which illuminates lunar surface on both sides of orbit, consequently, in a single-orbit observation, a detected target location has ambiguity in terms of the side of the orbit. However, use of multiple orbit data enables to resolve this ambiguity problem, i.e., radar illumination is controlled. We demonstrated this HF imaging technique by reconstructing surface images of Rupes Recta region using LRS observation data of 61 orbits. Control of radar illumination was confirmed by the presence/absence of Rupes Recta image in the reconstructed surface images. As it was anticipated, the reconstructed surface images presented false images which were identified as mirror images of major surface features. We also carried out simulation of these LRS observations of 61 orbits over the Rupes Recta site using Kirchhoff-approximation Surface Scattering (KiSS) simulation code. Comparison of the images of LRS observation and those of KiSS simulation exposed some discrepancies. Our interpretation is that the discrepancies are attributed to shallow subsurface scatterings which the KiSS simulation does not take into account. This implies the possibility of imaging shallow lava tubes by LRS, although we did not find one in this particular site of Rupes Recta region.

This paper proposes an alternative multi-level approach to duration prediction for improving prosody generation in statistical parametric speech synthesis using multiple Gaussian process experts. We use two duration models at different levels, specifically, syllable and phone. First, we individually train syllable- and phone-level duration models. Then, the predictive distributions of syllable and phone duration models are combined by product of Gaussians. The means of combined predictive distributions are used as predicted durations for synthetic speech. We show objective and subjective evaluation results for the proposed technique by comparing with the conventional ones when the techniques are applied to Gaussian process regression (GPR)-based speech synthesis.

This paper proposes a sentence selection technique for constructing phonetically and prosodically balanced compact recording scripts for speech synthesis. In the conventional corpus design of speech synthesis, a greedy algorithm that maximizes phonetic coverage is often used. However, for statistical parametric speech synthesis, balances of multiple phonetic and prosodic contextual factors are important as well as the coverage. To take account of both of the phonetic and prosodic contextual balances in sentence selection, we introduce an extended entropy of phonetic and prosodic contexts, such as biphone/triphone, accent/stress/tone, and sentence length. For detailed investigation, conventional and proposed techniques are evaluated using Japanese, English, and Chinese corpora. The objective experimental results show that the proposed technique achieves better coverage and balance of contexts. In addition, speech synthesis experiments based on hidden Markov models reveal that the generated speech parameters become closer to those of the natural speech compared with other conventional sentence selection techniques. Subjective evaluations show that the proposed sentence selection based on the extended entropy improves the naturalness of the synthetic speech while maintaining the similarity to the original sample.

In this paper, we propose a speaker adaptation technique for statistical parametric speech synthesis based on Gaussian process regression (GPR). Although it is reported that the GPR-based speech synthesis improves the naturalness of synthetic speech compared with the HMM-based speech synthesis, any speaker adaptation techniques for the GPR-based one have not been established. This is because GPR is a nonparametric model and hence it is impossible to directly apply linear transforms to model parameters. In the proposed technique, we introduce feature-space transform to achieve model adaptation in the framework of GPR-based speech synthesis. Experimental results of objective and subjective tests show that the proposed technique outperforms the conventional HMM-based speaker adaptation framework.

This paper proposes novel models of F0 contours and phone durations using Gaussian process regression and classification (GPR and GPC) for statistical parametric speech synthesis. Although the use of frame-based GPR has shown the effectiveness of spectral feature modeling in previous studies, the application of GPR to prosodic features, i.e., F0 and phone duration, was not investigated sufficiently because the kernel function was designed for phonetic information only. In this paper, therefore, we propose a kernel function available for multiple units such as syllables, moras, and accent phrases. The proposed kernel function is based on temporal acoustic events like the beginning of accent phrase and the relative position between the target frame and the event is utilized for the kernel function. Experimental results of objective and subjective tests show that the GPR/GPC-based F0 and duration modeling improves the prediction accuracy of acoustic features compared with HMM-based speech synthesis.

This paper examines two issues of a statistical speech synthesis approach based Gaussian process (GP) regression. Although GP-based speech synthesis can give higher performance in generating spectral parameters than the HMM-based one, a number of issues still remain. In this paper, we incorporate global variance (GV) feature to overcome over-smoothing problem into the parameter generation. Furthermore, in order to utilize an appropriate kernel function in accordance with actual data, we propose an EM-based kernel hyperparameter optimization technique. Objective and subjective evaluation results show that using GV and hyperparameter estimation enhanced the performance in spectral feature generation.

This paper proposes an automatic prosodic labeling technique for constructing speech database used for speech synthesis.In the corpus-based Japanese speech synthesis, it is essential to use annotated speech data with prosodic information such as phrase boundaries and accent types.However, manual annotation is generally time-consuming and expensive.To overcome this problem, we propose an estimation technique of accent types and phrase boundaries from speech waveform and its transcribed text using both language and acoustic models.We use conditional random field (CRF) for the language model, and HMM for the acoustic model which has shown to be effective in prosody modeling in speech synthesis.By introducing HMM, continuously changing features of F0 contours are modeled well and this results in higher estimation accuracy than conventional techniques that use simple polygonal line approximation of F0 contours.

This paper proposes a technique for creating target speaker's expressive-style model from the target speaker's neutral style speech in HMM-based speech synthesis. The technique is based on the style adaptation using linear transforms where speaker-independent transformation matrices are estimated in advance using pairs of neutral and target-style speech data of multiple speakers. By applying the obtained transformation matrices to a new speaker's neutral-style model, we can convert the style expressivity of the acoustic model to the target style without preparing any target-style speech of the speaker. In addition, we introduce a speaker adaptive training (SAT) framework into the transform estimation to reduce the acoustic difference among speakers. We subjectively evaluate the performance of the style conversion in terms of the naturalness, speaker similarity, and style reproducibility.

This paper proposes a new approach to text-to-speech based on Gaussian processes which are widely used to perform non-parametric Bayesian regression and classification. The Gaussian process regression model is designed for the prediction of frame-level acoustic features from the corresponding frame information. The frame information includes relative position in the phone and preceding and succeeding phoneme information obtained from linguistic information. In this paper, a frame context kernel is proposed as a similarity measure of respective frames. Experimental results using a small data set show the potential of the proposed approach without state-dependent dynamic features or decision-tree clustering used in a conventional HMM-based approach.

This paper proposes a technique for adding more prosodic variations to the synthetic speech in HMM-based expressive speech synthesis. We create novel phrase-level F0 context labels from the residual information of F0 features between original and synthetic speech for the training data. Specifically, we classify the difference of average log F0 values between the original and synthetic speech into three classes which have perceptual meanings, i.e., high, neutral, and low of relative pitch at the phrase level. We evaluate both ideal and practical cases using appealing and fairy tale speech recorded under a realistic condition. In the ideal case, we examine the potential of our technique to modify the F0 patterns under a condition where the original F0 contours of test sentences are known. In the practical case, we show how the users intuitively modify the pitch by changing the initial F0 context labels obtained from the input text.

This paper proposes a statistical nonparametric speech synthesis technique based on a sparse Gaussian process regression (GPR).In our previous study, we proposed GPR-based speech synthesis where each frame of synthesis units is modeled by a regression of Gaussian processes.Preliminary experiments of synthesizing several phones including both vowels and consonants showed a potential of the technique.In this paper, the previous work is extended to full-sentence speech synthesis using sparse GPs and context modification.Specifically, clusterbased sparse Gaussian processes such as local GPs and partially independent conditional (PIC) approximation are examined as a computationally feasible approach.Moreover, frame-level context is extended to include not only a position context from a current phone but also adjacent phones to generate smoothly changing speech parameters.Objective and subjective evaluation results show that the proposed technique outperforms the HMM-based speech synthesis with minimum generation error training.

This paper proposes a technique for effective modeling of F0 contours using prosodic-event-based HMM units for HMM-based spontaneous speech synthesis. The modeling unit corresponds to one of prosodic event segments such as pitch falling by accent and pitch rising by boundary pitch movement (BPM). Since the prosodic events of one phrase are generally less frequent than the changes of phonemes, the proposed unit is expected to reduce the number of model parameters of F0, which leads to robust parameter estimation. The objective and subjective experiments using spontaneous conversational speech data show that the proposed technique can significantly reduce the number of model parameters while keeping the naturalness of the synthetic speech.

This paper examines F0 modeling and generation techniques for spontaneous speech synthesis.In the previous study, we proposed a prosodic-unit HMM where the synthesis unit is defined as a segment between two prosodic events represented by a ToBI label framework.To take the advantage of the prosodicunit HMM, continuous F0 sequences must be modeled from discontinuous F0 data including unvoiced regions.The conventional F0 models such as the MSD-HMM and the continuous F0 HMM are not always appropriate for such demand.To overcome this problem, we propose an alternative F0 model named discontinuous observation HMM (DO-HMM) where the unvoiced frames are regarded as missing data.We objectively evaluate the performance of the DO-HMM by comparing it with the conventional F0 modeling techniques and discuss the results.

Synthetic aperture radar (SAR) processing was applied to the observation data of Lunar Radar Sounder (LRS), which is an HF sounder which was installed onboard a Japanese lunar exploration orbiter, Kaguya, for the purpose of imaging lunar subsurface structure. A two-media model was introduced to the LRS SAR algorithm to define the reference function of the LRS SAR processing. The LRS SAR algorithm has two free parameters, i.e., dielectric constant of the subsurface medium and synthetic aperture. The effect of these free parameters on LRS SAR imaging was studied by simulation and was verified by actual LRS observation data. A practical guideline for LRS SAR processing was drawn. The dielectric constant of the subsurface medium may be ignored in practice so far as the synthetic aperture is smaller than 10 km. For a larger synthetic aperture case, assumption of a moderate dielectric constant (ε = 6 ~ 8) of the subsurface medium is effective in realizing good focusing of deep targets. Finally, taking full advantage of ground processing, advanced processing was attempted. Off-nadir focusing SAR processing proved to be effective in imaging oblique objects whose dominant scattering angle was not the angle toward zenith. Changing the dielectric constant of the two-media model proved to be effective in focusing/defocusing small objects, thus enabling us to localize the object's position as surface or subsurface.

This paper proposes a technique for improving tone correctness in Thai speech synthesis based on an average voice model trained with nonprofessional speech corpus. The proposed technique utilizes quantized F0 symbols as the tonal context in order to obtain an appropriate F0 model. With this technique, the prosodic context can be extracted from real speech directly and this leads to prevent the inconsistency between speech data and F0 labels generated from transcription, which affects the naturalness and tone correctness in synthetic speech. We examine two types of tonal context labeling using the quantized F0 symbols based on phone and sub-phone boundaries. Experimental results of both objective and subjective tests show that the proposed technique can improve not only the naturalness but also the tone correctness of synthetic speech under condition of using a small amount speech data of nonprofessional target speakers.

This paper presents a very low bit-rate F0 coding technique for speaker-dependent phonetic vocoder based on hidden Markov model (HMM) using quantized F0 context. In the proposed technique, the input F0 sequence is converted into F0 symbol sequence at a phoneme level using scalar quantization. The quantized F0 symbols are used in the decoding process as the prosodic context for the HMM-based speech synthesis. The synthetic speech is generated from the context-dependent labels and input speaker's pre-trained HMMs by using the HMM-based parameter generation algorithm. By taking account account of preceding and succeeding phonemes and F0 symbols as the contextual factors, we can generate smooth F0 trajectory similar to that of the original with only a small number of quantization bits. Experimental results demonstrate that the proposed technique can generate F0 contour with acceptable quality even when the bit-rate is less than 50 bps.

This paper addresses an issue of prosodic variability of spontaneous speech in HMM-based spontaneous conversational speech synthesis.We propose an extended context set including peculiar information to spontaneous speech derived from the annotation data embedded in a large-scale of database of spontaneous Japanese.We show the effectiveness of the newly introduced contexts from the results of objective and subjective evaluation experiments.We also propose stopping criteria for decision-tree clustering to alleviate an over-fitting problem.Experimental results show that the restriction of the size of each leaf node can improve the quality of synthetic speech.

This paper describes an approach to HMM-based expressive speech synthesis which does not require any supervised labeling process for emphasis context. We use appealing-style speech whose sentences were taken from real domains. To reduce the cost for labeling speech data with an emphasis context for the model training, we propose an unsupervised labeling technique of the emphasis context based on the difference between original and generated F0 patterns of training sentences. Although the criterion for the emphasis labeling is quite simple, subjective evaluation results reveal that the unsupervised labeling is comparable to the labeling conducted carefully by a human in terms of speech naturalness and emphasis reproducibility. Index Terms: HMM-based speech synthesis, expressive speech, emphasis expression, unsupervised labeling, F0 generation

This paper proposes an HMM-based speech synthesis technique without any manual labeling of accent information for a target speaker's training data. To appropriately model the fundamental frequency (F0) feature of speech, the proposed technique uses coarsely quantized F0 symbols instead of accent types for the context-dependent labeling. By using F0 quantization, we can automatically conduct the labeling of F0 contexts for training data. When synthesizing speech, an average voice model trained in advance using manually labeled multiple speakers' speech data including accent information is used to create the label sequence for synthesis. Specifically, the input text is converted to a full context label sequence, and an F0 contour is generated from the label sequence and the average voice model. Then, a label sequence including the quantized F0 symbols is created from the generated F0 contour. We conduct objective and subjective evaluation tests, and discuss the results.

We propose a method for fast density estimation of samples, which makes it possible to significantly accelerate classification based on the k nearest neighbor (kNN) method. Our main premise is that many trials of a rough estimation of probability density function are conducted, and they are integrated by Bayes' theorem. The experimental results indicated that the classification time used in our method was at least 30 times faster than that of kNN.

Simultaneous observations of Lunar Radar Sounder (LRS) and Laser ALTimeter (LALT) of Kaguya, a Japanese lunar exploration project, were carried out for the purpose of mapping regolith layer thickness of the Moon. Nadir surface echo of a high-frequency (5 MHz) pulse of LRS interferes the shallow (<; 10 m) subsurface echo from the bottom of the regolith layer, which subsequently makes the apparent surface be detected at a range deviated from the actual surface range, while the actual surface range is optically detected by LALT. Regolith layer thickness information is retrieved from this range difference after an inversion process. So far, four major maria on the near side of the Moon (Maria Tranquillitatis, Serenitatis, Imbrium, and Oceanus Procellarum) have been investigated, and the mean regolith layer thicknesses of the four maria were found to be about the same, ranging from 6.3 to 6.9 m. However, spatial distribution of areal regolith thickness appears different in eastern maria from western maria, which implies a difference of the growth history of the regolith layer.

This paper proposes a technique for emotional speech recognition which enables us to extract paralinguistic information as well as linguistic information contained in speech signal. The technique is based on style estimation and style adaptation using multiple-regression HMM. Recognition process consists of two stages. In the first stage, a style vector that represents the emotional expression category and intensity of its variation of input speech is estimated on a sentence-by-sentence basis. Then the acoustic models are adapted using the estimated style vector and standard HMM-based speech recognition is performed in the second stage. We assess the performance of the proposed technique on the recognition of acted emotional speech uttered by both professional narrators and non-professional speakers and show the effectiveness of the technique.

In this paper, we analyze the effects of several factors and configuration choices encountered during training and model construction when we want to obtain better and more stable adaptation in HMM-based speech synthesis. We then propose a new adaptation algorithm called constrained structural maximumaposteriorilinear regression (CSMAPLR) whose derivation is based on the knowledge obtained in this analysis and on the results of comparing several conventional adaptation algorithms. Here, we investigate six major aspects of the speaker adaptation: initial models; the amount of the training data for the initial models; the transform functions, estimation criteria, and sensitivity of several linear regression adaptation algorithms; and combination algorithms. Analyzing the effect of the initial model, we compare speaker-dependent models, gender-independent models, and the simultaneous use of the gender-dependent models to single use of the gender-dependent models. Analyzing the effect of the transform functions, we compare the transform function for only mean vectors with that for mean vectors and covariance matrices. Analyzing the effect of the estimation criteria, we compare the ML criterion with a robust estimation criterion called structural MAP. We evaluate the sensitivity of several thresholds for the piecewise linear regression algorithms and take up methods combining MAP adaptation with the linear regression algorithms. We incorporate these adaptation algorithms into our speech synthesis system and present several subjective and objective evaluation results showing the utility and effectiveness of these algorithms in speaker adaptation for HMM-based speech synthesis.

We propose a technique for synthesizing speech with desired style expressivity of an arbitrary target speaker's voice. In an MLLR-based speaker adaptation technique for multiple regression hidden semi-Markov model (MRHSMM), the quality of synthesized speech crucially depends on the initial MRHSMM trained from a certain source speaker's data and it is not always possible to synthesize natural sounding speech with a given target speaker's voice. To overcome this problem, we perform simultaneous adaptation of speaker and style from an average voice model. Experimental results show that the proposed technique provides more natural sounding speech than the conventional one with speaker adaptation only.

This paper describes a speaker adaptation technique for style control based on multiple regression hidden semi-Markov model (MRHSMM), In the MRHSMM-based style control technique, when available training data is very small, the resultant model would produce unnatural sounding speech. To overcome this problem, we propose a model adaptation technique for MRHSMM, which is similar to the MLLR adaptation technique used in speech recognition and speech synthesis. We formulate the model adaptation problem for MRHSMM based on a linear transformation framework and derive re-estimation formulas for transformation matrices in ML sense. We also describe the results of subjective evaluation tests.

In human computer interaction and dialogue systems, it is often desirable for text-to-speech synthesis to be able to generate natural sounding speech with an arbitrary speaker's voice and with varying speaking styles and/or emotional expressions. We have developed an average-voice-based speech synthesis method using statistical average voice models and model adaptation techniques for this purpose. In this paper, we describe an overview of the speech synthesis system and show the current performance with several experimental results.

This paper presents a technique for estimating the degree or intensity of emotional expressions and speaking styles appeared in speech. The key idea is based on a style control technique for speech synthesis using multiple regression hidden semi-Markov model (MRHSMM), and the proposed technique can be viewed as the inverse process of the style control. We derive an algorithm for estimating predictor variables of MRHSMM each of which represents a sort of emotion intensity or speaking style variability appeared in acoustic features based on an ML criterion. We also show preliminary experimental results to demonstrate an ability of the proposed technique for synthetic and acted speech samples with emotional expressions and speaking styles.

In HMM-based speech synthesis, we have to choose the modeling strategy for speech synthesis units depending on the amount of available speech data to generate synthetic speech of better quality. In general, speaker-dependent modeling is an ideal choice for a large speech data, whereas speaker adaptation with average voice model becomes promising when available speech data of a target speaker is limited. This paper describes several speaker adaptation algorithms and MAP modification to develop consistent method for synthesizing speech in a unified way for arbitrary amount of the speech data. We incorporate these adaptation algorithms into our HSMM-based speech synthesis system and show its effectiveness from results of several evaluation tests

This paper describes a statistical approach for modeling and synthesizing human walking motion. In the approach, each motion primitive is modeled statistically from motion capture data using multiple regression hidden semi-Markov model (HSMM). HSMM is an extension of hidden Markov model (HMM), in which each state has an explicit state duration probability distribution, and multiple regression HSMM is the one whose mean parameter of probability distribution function is assumed to be given by a function of factors which affects human motion. In this paper, we introduce a training algorithm for the multiple regression HSMM, called factor adaptive training based on the EM algorithm and also describe a parameter generation algorithm from motion primitive HSMMs with prescribed values of factors. From experimental results, we show that the proposed technique can control walking movements in accordance with a change of the factors such as walking pace and stride length and can provide realistic human motion.

This paper presents a voiced/unvoiced determination algorithm using instantaneous frequency amplitude spectrum (IFAS) in adverse environments. The proposed algorithm measures the degree of periodicity of speech signal, defined as harmonicity measure, where the difference between voiced part and unvoiced speech can be quantitatively obtained. We describe a new technique for voicing decision using an IFAS-based F/sub 0/ evaluation function with variable window length and IF band selection. The proposed technique is evaluated with a speech signal corrupted by additive white Gaussian, pink, and traffic noise. The results show that the proposed method outperforms ESPS, AMDF and TEMPO for both female and male speakers in all simulated conditions.

This paper describes an adaptive training technique for hidden semi-Markov model (HSMM). The adaptive training scheme conducts normalization of speaker differences and acoustic variability in both output and state duration distributions of a canonical model by using HSMM-based MLLR (maximum likelihood linear regression) adaptation. We incorporate the adaptive training into our HSMM-based speech synthesis system with MLLR adaptation and compare synthesized speech using the adaptive training with that using standard speaker independent training. From the results of subjective tests, we show that the adaptive training outperforms speaker independent training and also show that the speech synthesis system generates speech with better naturalness and intelligibility than the original HSMM-based speech synthesis system.

This paper describes a technique based on spectral subtraction (SS) for enhancing audio signals which have nonstationary properties. In the technique, audio signals are modeled as a sum of sinusoidal components, which are called tones, of various frequencies and durations. We show that the analysis window length for SS should be adjusted in accordance with the tone duration to obtain higher performance on SS. Then we propose a multi-stage SS approach into which an appropriate window length selection mechanism is incorporated. We also propose a technique for suppressing an artifact called pre-echo which appears in the noise reduction process. It is shown from the result of a subjective evaluation test that the proposed technique gives superior performance to the conventional SS for enhancement of audio signals including speech, piano, drums, and orchestra.

This paper describes an MLLR-based speaking style adaptation technique for HMM-based speech synthesis. Since speaking styles and emotional expressions are characterized by many suprasegmental features as well as segmental features, it is necessary to adapt suprasegmental features for speaking style adaptation. To achieve suprasegmental feature adaptation, we utilize context clustering decision trees, which are constructed in the training stage, for tying of regression matrices. Using this technique, we adapt an initial "reading" style model to "joyful" or "sad" styles. Experimental results show that, using 50 adaptation sentences, speech samples generated from adapted models were judged to be similar to the target speaking styles at rates of 92% and 70% for joyful and sad styles, respectively.

This paper describes an approach to controlling style of synthetic speech in HMM-based speech synthesis. The style is defined as one of speaking styles and emotional expressions in speech. We model each speech synthesis unit by using a context-dependent HMM whose mean vector of the output distribution function is given by a function of a parameter vector called style control vector. We assume that the mean vector is modeled by multiple regression with the style control vector. The multiple regression matrices are estimated by EMalgorithm as well as other model parameters of HMMs. In the synthesis stage, the mean vectors are modified by transforming an arbitrarily given control vector which is associated with a desired style. The results of subjective tests show that we can control styles by choosing the style control vector appropriately.

This paper describes an extension of maximum likelihood linear regression (MLLR) to hidden semi-Markov model (HSMM) and presents an adaptation technique of phoneme/state duration for an HMM-based speech synthesis system using HSMMs. The HSMM-based MLLR technique can realize the simultaneous adaptation of output distributions and state duration distributions. We focus on describing mathematical aspect of the technique and derive an algorithm of MLLR adaptation for HSMMs.

In the present paper, a hidden-semi Markov model (HSMM) based speech synthesis system is proposed. In a hidden Markov model (HMM) based speech synthesis system which we have proposed, rhythm and tempo are controlled by state duration probability distributions modeled by single Gaussian distributions. To synthesis speech, it constructs a sentence HMM corresponding to an arbitralily given text and determine state durations maximizing their probabilities, then a speech parameter vector sequence is generated for the given state sequence. However, there is an inconsistency: although the speech is synthesized from HMMs with explicit state duration probability distributions, HMMs are trained without them. In the present paper, we introduce an HSMM, which is an HMM with explicit state duration probability distributions, into the HMM-based speech synthesis system. Experimental results show that the use of HSMM training improves the naturalness of the synthesized speech.

This paper describes the application of the notion of instantaneous frequency amplitude spectrum (IFAS) to discriminate voiced and unvoiced segments of speech signals. The classification procedures of speech signals into voiced and unvoiced is determined by using a harmonicity measure acquired after evaluating the instantaneous frequency amplitude spectrum. Several voicing decisions based on harmonicity measure are described to provide possible modifications including a post-processing stage to improve the classifier accuracy. The performance of the proposed method is also compared against ESPS and AMDF to demonstrate the effectiveness of the proposed method.

In this paper, we define an F0 quantization scheme for a very low bit rate speech coder based on HMM (hidden Markov model). In the coding system, the encoder carries out phoneme recognition, and transmits phoneme indices, state durations and F0 information to the decoder. In the decoder, phoneme HMM are concatenated according to the phoneme indices, and a sequence of mel-cepstral coefficient vectors is generated from the concatenated HMM. Finally we obtain synthetic speech by using the MLSA (mel log spectrum approximation) filter according to the mel-cepstral coefficients and F0 information. In addition to the F0 quantization, we investigate encoding methods for other parameters to reduce the bit rate, yet keeping the subjective speech quality. A subjective listening test shows that the performance of the proposed coder at about 100/spl sim/150 bit/s is superior to a VQ-based vocoder at 600 bit/s (mel-cepstrum: 6 bit/frame/spl times/50 frame/s, F0: 6 bit/frame/spl times/50 frame/s).

This paper describes a new training method of average voice model for speech synthesis in which an arbitrary speaker's voice is generated based on speaker adaptation. When the amount of training data is limited, the distributions of average voice model often have bias depending on speaker and/or gender and this will degrade the quality of synthetic speech. In the proposed method, to reduce the influence of speaker dependence, we incorporate a context clustering technique called shared decision tree context clustering and speaker adaptive training into the training procedure of the average voice model. From the results of subjective tests, we show that the average voice model trained using the proposed method generates more natural sounding speech than the conventional average voice model. Moreover, it is shown that voice characteristics of synthetic speech generated from the adapted model using the proposed method are closer to the target speaker than the conventional method.

In this paper, instantaneous frequency amplitude spectrum (IFAS)-based fundamental frequency estimator is evaluated with speech signal corrupted by additive white gaussian noise. A key idea of the IFAS-based estimator is the use of degree of regularity of periodicity in spectrum of speech signal, defined by a quantity called harmonicity measure, for band selection in the fundamental frequency estimation. Several frequency band and window length selection methods based on harmonicity measure are asessed to find out better performance. It is shown that the performance of the IFAS-based estimator is maintained at constant error rate about 1% from clean speech data up to 15 dB and about 11% at 0 dB SNR. For both female and male speakers, the IFAS-based estimator outperforms several well-known methods particularly at 0 dB SNR.

This paper describes a technique for estimating fundamental frequency of speech based on instantaneous frequency (IF). In the technique, fundamental frequency of a signal is obtained by evaluating its IF amplitude spectrum. We define a measure which represents how clearly the harmonic structure is shown in the IF amplitude spectrum. This harmonicity measure is used to select an optimal frequency band on which the IF amplitude spectrum is evaluated. We show the results of fundamental frequency estimation experiments using a speech database which contains 14 male and 14 female speakers' utterances. It is seen that the proposed technique can decrease gross errors substantially and obtain better performance than the conventional method based on the IF amplitude spectrum.

Presents an approach to modeling speech spectra and pitch for text-independent speaker identification using Gaussian mixture models based on multi-space probability distribution (MSD-GMM). The MSD-GMM allows us to model continuous pitch values for voiced frames and discrete symbols representing unvoiced frames in a unified framework. Spectral and pitch features are jointly modeled by a two-stream MSD-GMM. We derive maximum likelihood estimation formulae for the MSD-GMM parameters, and the MSD-GMM speaker models are evaluated for text-independent speaker identification tasks. Experimental results, show that the MSD-GMM can efficiently model spectral and pitch features of each speaker and outperforms conventional speaker models.

Describes a technique for synthesizing speech with arbitrary speaker characteristics using speaker independent speech units, which we call "average voice" units. The technique is based on an HMM-based text-to-speech (TTS) system and maximum likelihood linear regression (MLLR) adaptation algorithm. In the HMM-based TTS system, speech synthesis units are modeled by multi-space probability distribution (MSD) HMMs which can model spectrum and pitch simultaneously in a unified framework. We derive an extension of the MLLR algorithm to apply it to MSD-HMMs. We demonstrate that a few sentences uttered by a target speaker are sufficient to adapt not only voice characteristics but also prosodic features. Synthetic speech generated from adapted models using only four sentences is very close to that from speaker dependent models trained using 450 sentences.

This paper describes a technique for synthesizing speech with any desired voice. The technique is based on an HMM-based text-to-speech (TTS) system and MLLR adaptation algorithm. To generate speech of an arbitrarily given target speaker, speaker-independent speech units, i.e., average voice models, is adapted to the target speaker using MLLR framework. In addition to spectrum and pitch adaptation, we derive an algorithm for adaptation of state duration. We demonstrate that a few sentences uttered by a target speaker are sufficient to adapt not only voice characteristics but also prosodic features. Synthetic speech generated from adapted models using only four sentences is very close to that from speaker dependent models trained using a large amount of speech data.

This paper describes improvements on the excitation model of an HMM-based text-to-speech system. In our previous work, natural sounding speech can be synthesized from trained HMMs. However, it has a typical quality of “vocoded speech” since the system uses a traditional excitation model with either a periodic impulse train or white noise. In this paper, in order to reduce the synthetic quality, a mixed excitation model used in MELP is incorporated into the system. Excitation parameters used in mixed excitation are modeled by HMMs, and generated from HMMs by a parameter generation algorithm in the synthesis phase. The result of a listening test shows that the mixed excitation model significantly improves quality of synthesized speech as compared with the traditional excitation model.

This paper derives a speech parameter generation algorithm for HMM-based speech synthesis, in which the speech parameter sequence is generated from HMMs whose observation vector consists of a spectral parameter vector and its dynamic feature vectors. In the algorithm, we assume that the state sequence (state and mixture sequence for the multi-mixture case) or a part of the state sequence is unobservable (i.e., hidden or latent). As a result, the algorithm iterates the forward-backward algorithm and the parameter generation algorithm for the case where the state sequence is given. Experimental results show that by using the algorithm, we can reproduce clear formant structure from multi-mixture HMMs as compared with that produced from single-mixture HMMs.

This paper presents an approach to estimating the parameters of continuous density HMMs for visual speech recognition. One of the key issues of image-based visual speech recognition is normalization of lip location and lighting conditions prior to estimating the parameters of HMMs. We presented a normalized training method in which the normalization process is integrated in the model training. This paper extends it for contrast normalization in addition to average-intensity and location normalization. The proposed method provides a theoretically-well-defined algorithm based on a maximum likelihood formulation, hence the likelihood for the training data is guaranteed to increase at each iteration of the normalized training. Experiments on the M2VTS database show that the recognition performance can be significantly improved by the normalized training.

This paper describes security of speaker verification systems against imposture using synthetic speech. We propose a text-prompted speaker verification technique which utilizes pitch information in addition to spectral information, and investigate whether synthetic speech is rejected. Experimental results show that pitch information is not necessarily useful for rejection of synthetic speech, and it is required to develop techniques to discriminate synthetic speech from natural speech.

This paper discusses a hidden Markov model (HMM) based on multi-space probability distribution (MSD). The HMMs are widely-used statistical models to characterize the sequence of speech spectra and have successfully been applied to speech recognition systems. From these facts, it is considered that the HMM is useful for modeling pitch patterns of speech. However, we cannot apply the conventional discrete or continuous HMMs to pitch pattern modeling since the observation sequence of the pitch pattern is composed of one-dimensional continuous values and a discrete symbol which represents "unvoiced". MSD-HMM includes discrete HMMs and continuous mixture HMMs as special cases, and further can model the sequence of observation vectors with variable dimension including zero-dimensional observations, i.e., discrete symbols. As a result, MSD-HMMs can model pitch patterns without heuristic assumption. We derive a reestimation algorithm for the extended HMM and show that it can find a critical point of the likelihood function.

We present a new self-organizing neural network which performs unsupervised learning and can be used for vector quantization. The main advantage over existing approaches, e.g., the Kohonen feature map, is the ability of the model to automatically find a suitable network structure and size. This is achieved through a controlled growth process which also includes occasional removal of units. The algorithm is evaluated on a database that includes 25 speakers each of them recorded in 12 diffrent sesions. The overall performance was 99.5%. That is, in 99.5% of the trials, the right speaker was correctly accepted and the impostor speaker correctly rejected.

This paper proposes a wideband CELP coder using frequency warping. Instead of linear prediction, the proposed coder adopts the mel-generalized cepstral analysis, and encodes the fullband of the speech signal through a warped frequency scale. It is shown that the subjective quality of the proposed coder at 16 kbit/s is better than that of the ITU-T G.722 at 64 kbit/s. Furthermore, the proposed coder gives a much smaller difference in performance for male and female speakers than the conventional CELP coder. These results indicate that the frequency warping makes a large contribution to the improvement of the subjective quality for wideband speech coding.

This paper presents a new technique for synthesizing visual speech from arbitrarily given text. The technique is based on an algorithm for parameter generation from HMM with dynamic features, which has been successfully applied to text-to-speech synthesis. In the training phase, syllable HMMs are trained with visual speech parameter sequences that represent lip movements. In the synthesis phase, a sentence HMM is constructed by concatenating syllable HMMs corresponding to the phonetic transcription for the input text. Then an optimum visual speech parameter sequence is generated from the sentence HMM in an ML sense. The proposed technique can generate synchronized lip movements with speech in a unified framework. Furthermore, coarticulation is implicitly incorporated into the generated mouth shapes. As a result, synthetic lip motion becomes smooth and realistic.

This paper presents a very low bit rate speech coder based on HMM (hidden Markov model). The encoder carries out phoneme recognition, and transmits phoneme indexes, state durations and pitch information to the decoder. In the decoder, phoneme HMMs are concatenated according to the phoneme indexes, and a sequence of mel-cepstral coefficient vectors is generated from the concatenated HMM by using an ML-based speech parameter generation technique. Finally we obtain synthetic speech by exciting the MLSA (mel log spectrum approximation) filter, whose coefficients are given by mel-cepstral coefficients, according to the pitch information. A subjective listening test shows that the performance of the proposed coder at about 150 bit/s (for the test data including 26% silence region) is comparable to a VQ-based vocoder at 400 bit/s (=8 bit/frame/spl times/50 frame/s) without pitch quantization for both coders.

We have proposed a wideband CELP coder, called MGC-CELP, which provides high quality speech by utilizing mel-generalized cepstral (MGC) analysis instead of linear prediction (LP). In this paper, we investigate the performance of the wideband MGCCELP coder at 16 kbit/s in terms of short-term predictor order, i.e., order of MGC analysis. Subjective tests show that the MGCCELP coder with a predictor of order 20 gives better performance than ITU-T G.722 at 64 kbit/s. It is also found that the MGCCELP coder with 12th order achieves comparable quality to the 64 kbit/s G.722, and outperforms the 16 kbit/s conventional CELP coder using 20th-order LP analysis under the same conditions. 1. INTRODUCTION Recently several schemes for high-quality wideband speech coding at low bit rates have been developed. Most of the work in this field uses either transform/subband coding or CELP (Code Excited Linear Prediction) coding. At the bit rates around 16 kbit/s, CELP coding has received much attention s...

This paper describes a speaker adaptation technique for a phonetic vocoder based on HMM. In the vocoder, the encoder performs phoneme recognition and transmits phoneme indexes and state durations to the decoder, and the decoder synthesizes speech using HMM-based speech synthesis technique. One of the main problems of this vocoder is that the voice characteristics of synthetic speech depend on HMMs used in the decoder, and are therefore fixed regardless of a variety of input speakers. To overcome this problem, we adapt HMMs to input speech by transmitting transfer vectors, information on mismatch between the input speech and HMMs. The results of the subjective tests show that the performance of the proposed vocoder without quantization of transfer vectors is comparable to that of a speaker dependent vocoder. 1. INTRODUCTION To code speech at rates on the order of 100 bit/s, phonetic or segment vocoders are the most popular techniques [1]-[6]. These coders decompose speech into a seque...

The performance of several algorithms for the quantization of the mel-generalized cepstral coefficients is studied. First, the objective and subjective performance of two-stage vector quantization (VQ) is measured. It is shown that the subjective quality for the mel-generalized cepstral coefficients is higher than that for LSP. Secondly, interframe prediction is introduced in the encoding of mel-generalized cepstral coefficients. By utilizing interframe moving average (MA) prediction, the mel-generalized cepstral coefficients can be encoded more efficiently than LSP in terms of cepstral distortion. Finally, we implement a CELP coder based on mel-generalized cepstral analysis in which mel-generalized cepstral coefficients are quantized using MA prediction. This coder has a higher objective quality than conventional CELP.

We describe an approach to voice characteristics conversion for an HMM-based text-to-speech synthesis system. Since this speech synthesis system uses phoneme HMMs as speech units, voice characteristics conversion is achieved by changing the HMM parameters appropriately. To transform the voice characteristics of synthesized speech to the target speaker, we applied the maximum a posteriori estimation and vector field smoothing (MAP/VFS) algorithm to the phoneme HMMs. Using 5 or 8 sentences as adaptation data, speech samples synthesized from a set of adapted tied triphone HMMs, which have approximately 2,000 distributions, are judged to be closer to the target speaker by 79.7% or 90.6%, respectively, in an ABX listening test.

This paper presents a new text-to-speech synthesis system based on HMM which includes dynamic features, i.e., delta and delta-delta parameters of speech. The system uses triphone HMMs as the synthesis units. The triphone HMMs share less than 2,000 clustered states, each of which is modelled by a single Gaussian distribution. For a given text to be synthesized, a sentence HMM is constructed by concatenating the triphone HMMs. Speech parameters are generated from the sentence HMM in such a way that the output probability is maximized. The speech signal is synthesized directly from the obtained parameters using the mel log spectral approximation (MLSA) filter. Without dynamic features, the discontinuity of the generated speech spectra causes glitches in the synthesized speech. On the other hand, with dynamic features, the synthesized speech becomes quite smooth and natural even if the number of clustered states is small.

This paper proposes a technique for estimating the harmonic frequencies based on instantaneous frequency (IF) of a speech signal. The main problem is how to decompose the speech signal into the harmonic components. For this purpose, we use a set of bandpass-filters, each of whose center frequencies changes with time in such a way that it tracks the instantaneous frequency of its output. As a result, the outputs of the band-pass filters become the harmonic components, and the instantaneous frequencies of the harmonics are accurately estimated. To evaluate the effectiveness of the approach, we apply it to pitch extraction. The pitch extraction is simply accomplished by selecting the correct fundamental frequency out of the harmonic frequencies. The most significant feature of the pitch extractor is that the extracted pitch contour is smooth and it requires no post-processing such as nonlinear filtering or any smoothing processes.

We propose a CELP coder based on mel-cepstral analysis. In the coder, since the transfer functions of perceptual weighting and postfiltering are defined through mel-cepstral coefficients, the effects of perceptual weighting and postfiltering should fit with the characteristics of the human auditory sensation. We use a basic CELP structure without adaptive codebook, and the subjective speech quality of the proposed coder in terms of the opinion equivalent Q is measured and compared with that of the conventional CELP coder. It is shown that the improvement of more than 1.8 dB is achieved by the proposed coder over the conventional CELP coder.

This paper proposes an algorithm for speech parameter generation from HMMs which include the dynamic features. The performance of speech recognition based on HMMs has been improved by introducing the dynamic features of speech. Thus we surmise that, if there is a method for speech parameter generation from HMMs which include the dynamic features, it will be useful for speech synthesis by rule. It is shown that the parameter generation from HMMs using the dynamic features results in searching for the optimum state sequence and solving a set of linear equations for each possible state sequence. We derive a fast algorithm for the solution by the analogy of the RLS algorithm for adaptive filtering. We also show the effect of incorporating the dynamic features by an example of speech parameter generation.

This paper proposes an algorithm for speech parameter generation from continuous mixture HMMs which include dynamic features, i.e., delta and delta-delta parameters of speech. We showthatthe parameter generation from HMMs using the dynamic features results in searching for the optimal state sequence and solving a set of linear equations for each possible state sequence. Tosolve the problem, we derivea fast algorithm on the analogy of the RLS algorithm for adaptive #ltering. We show that the generated speech parameter vectors re#ect not only the means of static and dynamic feature vectors but also the covariances of those. An example presenting e#ectiveness of the proposed algorithm in speech synthesis is given. 1. INTRODUCTION The hidden Markov models #HMMs# can model sequences of speech spectra with well-de#ned algorithms, and have successfully been applied to speech recognition systems. From these facts, we surmise that HMMs are also useful for speech synthesis. Actually, some at...

This paper proposes an algorithm for adaptive cepstral analysis based on the UELS (unbiased estimation of log spectrum). In the UELS, the model spectrum is represented by cepstral coefficients and the mean square of the inverse filter output is minimized with respect to the cepstral coefficients. By introducing an instantaneous gradient estimate of the criterion in a similar manner of the LMS algorithm, we develop an adaptive cepstral analysis algorithm. In the analysis system, an IIR adaptive filter whose coefficients are given by cepstral coefficients is realized using the log magnitude approximation (LMA) filter. The filter approximates an exponential transfer function and its stability is guaranteed for approximation of speech spectra. To implement the M th order cepstral analysis, the algorithm requires O(M) operations per sample. It is shown that the algorithm has fast convergence properties in comparison with the LMS algorithm. Several examples of the adaptive cepstral analysis for synthetic signal and natural speech are shown to demonstrate the effectiveness of the algorithm.

We propose an ADPCM coder which uses a backward adaptive predictor based on the adaptive mel-cepstral analysis. The spectrum represented by the mel-cepstral coefficients has frequency resolution similar to that of the human ear which has high resolution at low frequencies. In the coder, since the transfer functions of noise shaping and postfiltering are also defined through the mel-cepstral coefficients, the effects of nose shaping and postfiltering should fit with characteristics of the human auditory sensation. We incorporate a pitch predictor into the ADPCM coder, and evaluate the speech quality based on objective and subjective performance tests. It is shown that the coder at 16 kb/s can produce a high quality speech comparable with that of the CCITT G.721 ADPCM coder at 32 kb/s with no algorithmic delay.>

The authors describe a mel-cepstral analysis method and its adaptive algorithm. In the proposed method, the authors apply the criterion used in the unbiased estimation of log spectrum to the spectral model represented by the mel-cepstral coefficients. To solve the nonlinear minimization problem involved in the method, they give an iterative algorithm whose convergence is guaranteed. Furthermore, they derive an adaptive algorithm for the mel-cepstral analysis by introducing an instantaneous estimate for gradient of the criterion. The adaptive mel-cepstral analysis system is implemented with an IIR adaptive filter which has an exponential transfer function, and whose stability is guaranteed. The authors also present examples of speech analysis and results of an isolated word recognition experiment.>

A technique for designing two-dimensional (2-D) digital filters which can approximate arbitrary magnitude functions is proposed. A 2-D spectral factorization technique is used to obtain the recursively computable and stable system with nonsymmetric half-plane support from a given 2-D magnitude function. A new class of realizable 2-D digital filters referred to as 2-D log magnitude approximation (2-D LMA) filters are used to approximate the system obtained by the 2-D factorization. The design procedure is straightforward and computationally efficient. A simple stability condition which guarantees the stability of the designed 2-D LMA filter is given. An efficient network structure of 2-D LMA filters for parallel implementation is also discussed.>

A technique for designing IIR (infinite impulse response) filters with a complex-valued frequency response is proposed. The complex logarithmic error, whose real and imaginary parts represent the log magnitude and phase errors, respectively, is minimized in the Chebyshev sense using an iterative weighted-least-squares (WLS) technique in the frequency domain. Both the log magnitude and phase errors of the resulting filter become nearly equiripple. Filter coefficients at each iteration are efficiently computed using a fast recursive algorithm for a set of linear equations which is derived from the WLS problem.>

An adaptive cepstral analysis method based on an unbiased estimation of the log spectrum is proposed. In the method, an infinite impulse response adaptive filter whose coefficients are given by cepstral coefficients is realized using the log magnitude approximation (LMA) filter. To implement the Mth-order cepstral analysis, the algorithm requires O(M) operations per sample. It is shown that the algorithm has fast convergence properties in comparison with the least-mean-square algorithm. A real-time analysis system is implemented with a general-purpose digital signal processor, and an example of natural speech analysis is shown to demonstrate the convergence.>

A technique is proposed for designing autoregressive-moving average (ARMA) digital filters to have an arbitrary log magnitude frequency response. The technique is based on an iterative weighted least-squares (WLS) approach in the frequency domain. A weight updating procedure is introduced to obtain a least-squares approximation to the given log magnitude function using the WLS approach. Filter coefficients are efficiently calculated using a fast recursive algorithm for a set of linear equations derived from the WLS problem. The technique is also extended to equiripple approximation with a minor modification of the weight updating procedure.>

Perturbations in the sequences of pitch periods and amplitudes of the signals picked up by a throat contact microphone are investigated for the purpose of the detection of laryngeal pathology with special emphasis on the detection of cancer of the vocal folds. The subjects consist of 101 patients with various laryngeal pathologies, including 42 patients with cancer of the vocal folds, and 39 normal speakers for control purposes. The data are statistically analyzed with two acoustic measures: Koike's quotient[3] and a novel index which is derived from reflection coefficients obtained by the maximum entropy method for spectral estimation of the sequences. It is shown that when the TNM classification of UICC is employed to describe the physiological status of the larynx of patients with cancer of the vocal folds, the T2-T4 cancers can reliably be detected with each of the two measures.

This paper will describes a digital speech signal processor(DSSP) designed for a narrow band and a medium band speech coding system, both of them are recently developed at the E.C.L., N.T.T.. The narrow band system is LSP(Line Spectrum Pair) vocoder, which is based on the frequency domain representation of LPC parameters. The LSP parameters are superior to conventional LPC parameters in view of its quantization and interpolation properties. The medium band system is a split band adaptive predictive coding with adaptive bit-allocation, APC-AB. This system can achieve a toll quality coding at 16 Kbit/s. We have made an extensive software simulation of LSP and APC-AB, and decided the specification of the DSSP. The DSSP can handle arithmetic and shifting operations in both 12 and 24 bit mode. Multiplications are done by a 12- by-12 parallel multiplier. Multiplier, ALU and shifters can operate concurrently in a pipeline mode at the minimum interval of 0.5 µs per multiplication-accumulation.