We propose a novel method of efficiently searching very large populations of speakers, tens of thousands or more, using an utterance comparison model proposed in a previous work. The model allows much more efficient comparison of utterances compared to the traditional Gaussian Mixture Model(GMM)-based approach because of its computational simplicity while maintaining high accuracy. Furthermore, efficiency can be drastically improved when approximating searches using kernelized locality-sensitive hashing (KLSH). From a speaker's utterance, a set of statistics are extracted according to the utterance comparison model and converted to a set of hash key bits. An Approximate Nearest Neighbor search using the Hamming Distance can be done to find candidate matches with the query speaker, which are then rank-ordered by linearly comparing them with the query using the utterance comparison model. Compared to GMM-based speaker identification and some of its variants that have been proposed to increase its efficiency, the proposed KLSH-based method is orders of magnitude faster while compromising a negligible amount of accuracy for sufficiently long query utterances. At a more fundamental level, we also discuss how our speaker matching framework differs from the traditional Bayesian decision rule used for speaker identification.

Wideband audio signal will be commonly used in the near future telecommunication applications. In quiet environments, the speech recognition performance increases when using a wideband signal instead of a narrowband signal. For practical ASR systems, however, we are interested in whether we can benefit from a wideband signal when recognizing noisy speech. The wideband speech signal, with respect to the narrowband speech signal, contains high frequency components, which usually have low intensity and, thus, are vulnerable to noise distortions. The robustness of the wideband feature set may be worse than that of the narrowband feature set, if the added high-frequency components are distorted. We investigate whether the addition of information from high frequencies into the ASR feature set can improve the recognition performance of noisy speech. The differences between low- and high-frequency parts of the wideband speech spectrum suggest a separate processing of these two parts. We propose an algorithm in which the separate processing scheme permits us to reuse the noise robust front-end originally designed for a narrowband signal. A low complexity processing is designed for high frequency components, which usually bear less information. The high-frequency information is added to the narrowband speech features in a form of de-noised filter-bank energies. The energies are appended after or before computing the cepstral features. In the best case, we obtained 13.96% average relative improvement when recognizing wideband noisy speech with respect to the narrowband noisy speech performance. The proposed algorithm is part of the recently adopted ETSI standard for the advanced frontend for distributed speech recognition.

We introduce a new concept in advancing the noise robustness of a speech recognition front-end. The presented method, called SNR-dependent waveform processing (SWP), exploits SNR variability within a speech period for enhancing the high SNR period portion and attenuating the low SNR period portion in the waveform time domain. In this way, the overall SNR of noisy-speech is increased, and at the same time, the periodicity of voiced speech is enhanced. This approach differs significantly from the well-known speech enhancement techniques, which are mostly frequency domain based, and we use it in this work as a complementary technique to them. In tests with SWP, we present significant clean and noisy speech recognition performance gains using the AURORA 2 database and recognition system as defined by ETSI for the robust frontend standardization process. Moreover, the presented algorithm is very simple and it is attractive also in terms of computational load.

This paper presents the robust front-end algorithm that was submitted by Motorola to the ETSI STQ-Aurora DSR working group as a proposal for the Advanced DSR front-end in January 2001. The algorithm consists of a two-stage melwarped Wiener filter, a waveform processor, a channelnormalized mel-frequency cepstral calculation and a subsystem of post-cepstral processing according to the reliability of mel-spectral components, etc. The output of this algorithm, a set of Mel-Frequency Cepstral Coefficients (MFCC), is compressed and encoded based on ETSI ES 201 108 standard; and then it is transmitted at 4800 bps. Compared to ETSI standard MFCC front-end, the proposed algorithm delivers an improvement of 42.64% in performance on the Aurora 2 database, which is required by this Eurospeech Special Event. With a very simple frame deletion algorithm based on a Voice-Onset Detection (VOD), the improvements were significantly boosted to 47.58% on the same database. In this paper we also give further insights about the proposal by providing performances and analyses with the Aurora SpeechDat-Car databases.

We present an algorithm to generate wideband speech from a narrow band version of the same. The main body of the algorithm is a statistical recovery function (SRF), which predicts the highband spectrum based solely on the narrowband spectrum. The performance of the algorithm has been measured both in terms of spectral distortion and spectral signal-to-noise ratio (SNR). We obtained a 3 dB gain in SNR for the reconstructed wideband speech as compared to the narrowband speech.>

Algorithms for encoding speech with good intelligence and naturalness at very low rates are studied. Naturalness is retained by encoding accurately the speech excitation information from an LPC (linear predictive coding) model. A glottal ARX (autoregressive with exogenous input) technique is used to model the speech signal for high quality. A large reduction in coding rate is achieved through short-term temporal compression of the speech and vector quantization. Application of traditional vector quantization to the temporal decomposition output is discussed, with consideration of distortion measures and codebook generation. Based on properties of short-term temporal decomposition, finite-state vector quantization is introduced to further decrease the coding rate. A problem associated with this technique, estimation of a state transition matrix with incomplete data, is treated. The general result is that practical coders operating in a range of 450-600 b/s with a delay of about 200 ms and natural-sounding output speech can be designed.>

The authors have assessed the possibility of modeling phone trajectories to accomplish speech recognition. This approach has been considered as one of the ways to model context-dependency in speech recognition based on the acoustic variability of phones in the current database. A hybrid segmental learning vector quantization/hidden Markov model (SLVQ/HMM) system has been developed and evaluated on a telephone speech database. The authors have obtained 85.27% correct phrase recognition with SLVQ alone. By combining the likelihoods issued by SLVQ and by HMM, the authors have obtained 94.5% correct phrase recognition, a small improvement over that obtained with HMM alone.>

We view the speech enhancement task in two aspects: reduction of the perceptual noise level in degraded speech and reconstruction of the degraded information, which may result in improvement of speech intelligibility. We are also very interested in noiseindependent speech enhancement where test noise environments could differ in intensity from those of algorithm development. To this end, we have developed in this paper an algorithm called Noise-Independent Statistical Spectral Mapping (NISSM) to estimate a speech enhancement Wiener filter. NISSM consists of a noise-resistant transformation, which converts noisy speech to a set of noise-resistant features, and a spectral mapping function, which maps the features to autoregressive spectra of clean speech. We will show that the proposed algorithm effectively reduces noise intensity. When the noise intensity of training differs from that of testing, NISSM outperforms significantly a conventional spectral mapping. The algorithm operates frame-by-frame and is designed for real-time application. The noise interference could be stationary or non-stationary white noise with variable intensity. I.

We present an algorithm to generate wideband speech from a narrow band version of the same. The main body of the algorithm is a statistical recovery function (SRF), which predicts the highband spectrum based solely on the narrowband spectrum. The performance of the algorithm has been measured both in terms of spectral distortion and spectral signal-to-noise ratio (SNR). We obtained a 3 dB gain in SNR for the reconstructed wideband speech as compared to the narrowband speech. >

The problem of enhancing speech degraded by stationary and nonstationary additive white noise is addressed. The authors have explored a new idea, enhancing speech based on auditory evidence, for this traditional topic. Distinguishing different objectives for heavy and light noise interference, two related algorithms have been developed. For speech degraded by heavy noise, the improvement in signal-to-noise ratio (SNR) is as high as 12 dB; for lightly noisy speech, the improvement is modest and decreases as the SNR of the noisy speech increases. Quantizing noise is used to assess the capacity of reducing nonstationary noise with the algorithms; a significant reduction of such noise and an improvement in speech quality have been achieved. The advantages of the proposed algorithms for speech enhancement include no need for prior knowledge of the noise and only a modest computational requirement.>

An ARX model with glottal excitation is used to achieve an accurate and economical frequency compression of speech, and a short-term temporal decomposition is used to do temporal compression. Quantization properties of the new set of coefficients and of the side information are studied through vector quantization. Moreover, use is made of the sequential structure of the vectors produced by a short-term temporal decomposition to decrease further the coding bit rate. As an application of the above techniques, a 450-b/s vocoder with a 200-ms delay is described.>

The authors demonstrate theoretically that an ARX model with glottal excitation can take into account segmentation error, but this introduces a perturbation of model parameters. A practical investigation of parameter sensitivity due to the error is reported. The parameter sensitivity can be viewed in terms of the robustness of the estimation process. The authors present a robust estimation procedure to decrease the sensitivity without an increase in computation.>

The shape of the vocal tract and its dynamic transitions convey an essential part of the phonetic information in speech. Therefore the way to represent this shape is important for the quality of speech coding or speech synthesis. In this paper, we first study the influence of the spatial sampling of the area function upon the acoustic characteristics of the vocal tract, and determine an optimal sampling step minimizing the computational cost without perceptual degradation. In a second part, we deal with the problem of continuous variation of the vocal tract length for a KELLY-LOCHBAUM reflexion line analog simulation: we propose a method of temporal sampling frequency conversion.

In this paper, we briefly study the effects of source-tract interaction on the waveshape of real glottal pulses. We simulate these effects in an formant synthesizer by using a dynamic variation of the voiced excitation and periodically time-varying formant filters.