Domain-specific problems where data collection is an expensive task are often represented by scarce or incomplete data. From a machine learning perspective, this type of problems has been addressed using models trained in different specific domains as the starting point for the final objective-model. The transfer of knowledge between domains, known as transfer learning (TL), helps to speed up training and improve the performance of the models in problems with limited amounts of data. In this letter, we introduce a TL approach to classify isolated volcano-seismic signals at “Volcán de Fuego”, Colima (Mexico). Using the well-known convolutional architecture (LeNet) as a feature extractor and a representative data set containing regional earthquakes, volcano-tectonic earthquakes, long-period events, volcanic tremors, explosions, and collapses, our proposal compares the generalization capabilities of the models when we only fine-tune the upper layers and fine-tune overall of them. Compared with the other state-of-the-art techniques, classification systems based on TL approaches provide good generalization capabilities (attaining nearly 94% of events correctly classified) and decreasing computational time resources.

This paper proposes TELIAMADE (an indoor location system based on ultrasonic and radiofrequency signals) to be used as a teaching tool in the context of Telecommunication Engineering. Due to its simple design, the versatility of its configuration and the characteristics of the involved signals, TELIAMADE is an appropriate tool for teaching basic aspects in location systems, digital communication systems, encoded signalling, microcontroller programming, radio protocols or advanced signal processing techniques. The TELIAMADE design allows students to sample, store and analyze signals at different points of the circuits by using conventional oscilloscopes. Furthermore, some parameters can be configured, allowing students to assess the advantages and inconveniences of each specific configuration with respect to features such as bit-rate, range, robustness against noise or updating period. Our system presents advantages in the field of teaching for understanding commercial systems for location (like GPS) or communication (like wireless digital communication systems).

The objective of this paper is to analyze the performance of an indoor positioning system based on the ultrasound level, usually called RSSI (received signal strength indicator). This paper builds on past experiences where RSSI values from beams sent in different directions were compared [1]. There relative values of RSSI were measured but herein we go one step further and perform localization based on absolute values of RSSI. The use of RSSI could potentially mean a simplification of the traditional positioning systems design in relation to other methods such as TOF (time-of-flight), being an alternative and interesting method for positioning. The distance between nodes (transmitter and receiver) is estimated from RSSI values using a signal propagation model in which the power losses due to the spherical divergence and atmospheric absorption are considered. However, in real conditions, other factors related to the characteristics of the nodes have their impact on RSSI measurements. One of them has to do with the beamwidth of the transducers. The signal strength will be conditioned by the angle of incidence of the transducers when these have a narrow beamwidth. On the other hand, the common use of wireless nodes in the deployment of these systems leads to the RSSI measurement to be affected by the battery level in the nodes. These effects introduce significant errors in the distance estimation and therefore on the localization precision. Herein we propose a mechanism for modeling the power loss due to the orientation of the ultrasonic transducers, as well as an algorithm to compensate for the effect of the variations in the battery level on RSSI measurements. Some experimental results have been obtained with 5 nodes (4 transmitters and 1 receiver) to show the quality of our compensation using a real positioning system. Location errors smaller than 10 cm for each coordinate are obtained in contrast to errors of several meters which are normally attained in this kind of systems based on RF (radio frequency) RSSI-values.

This letter assesses an improved equalization transformation for robust speech recognition in noisy environments. The proposal is an evolution of the parametric approximation to Histogram Equalization named PEQ into a two-step algorithm dealing separately with environmental and acoustic mismatch. A first parametric equalization is done to eliminate environmental mismatch. These equalized data are divided into classes, and parametrically re-equalized using class specific references to reduce the acoustic mismatch. Experiments have been conducted for Aurora 2 and Aurora 4 databases. A comparative analysis of the experimental results shows significant benefits for databases with high acoustic variability like Aurora 4.

This paper describes the conceptual and algorithmic evolutions of Memory Based Parametric Equalization (MPEQ) needed to exploit the potentialities of the method within the state-of-the-art Loquendo ASR. MPEQ is the memory-based evolution of Parametric Non-Linear Equalization (PEQ) introduced to overcome the problem of unreliable statistics estimation in presence of very limited acoustic information in the test utterance to be normalized. The main limitations of the method that prevented its practical application were the lack of online implementation, the unrealistic unimodal assumption about the training statistics, the unconditioned application of equalization, and the need for retraining the acoustic models. The paper describes how these limitations have been overcome and reports a large experimentation on many corpora that shows improvements in a variety of mismatched conditions, while preserving performances in matched conditions.

This work deals with strategies to jointly reduce the speaker and environment mismatches in Automatic Speech Recognition. The consequences of environmental mismatch in the performance of conventional Vocal Tract Length Normalization algorithm are analyzed, observing the sensitivity of the warping factor distributions to the SNR fall. A new combined speaker-noise normalization strategy which reduces the effect of noise in VTLN by applying Histogram Equalization is proposed and experimented in AURORA2 and AU RORA4 databases. Solid results are obtained and discussed to analyze the effectiveness of the described technique.

This paper analyzes the benefits and drawbacks of PEQ, a features normalization technique based on the parametric equalization of the MFCC parameters to match a reference probability distribution. Three evolutions of PEQ are presented as solutions to the limitations encountered. The effects of those proposed evolutions are studied on three speech corpora, namely WSJ0, AURORA-3 and HIWIRE cockpit database. Simple and more complex evaluation tasks with different mismatch conditions given by convolutional and/or additive noise and non-native speakers, are evaluated to analyze the improvements.

This paper shows a revised statistical test for voice activity detection in noise adverse environments. The method is based on a revised contextual likelihood ratio test (LRT) defined over a multiple observation window. The new approach not only evaluates the two hypothesis consisting on all the observations to be speech or nonspeech but all the possible hypothesis defined over the individual observations. The implicit hangover mechanism artificially added by the original method was not found in the revised method so its design can be further improved. With these and other innovations the proposed method showed a high speech/non-speech discrimination over a wide range of SNR conditions. The experimental framework showed that the revised method yields significant improvements over standardized VADs for discontinuous voice transmission and distributed speech recognition, as well as over recently reported methods.

In this paper, a unified approach to speech enhancement, feature extraction and feature normalization for speech recognition in adverse recording conditions is presented. The proposed frontend system consists of several different, independent, processing modules. Each of the algorithms contained in these modules has been independently applied to the problem of speech recognition in noise, significantly improving the recognition rates. In this work, these algorithms are merged in a single front-end and their combined performance is demonstrated. Specifically, the proposed advanced front-end extracts noise-invariant features via the following modules: Wiener filtering, voice-activity detection, robust feature extraction (nonlinear modulation or fractal features), parameter equalization and frame-dropping. The advanced front-end is applied to extremely adverse environments where most feature extraction schemes fail. We show that by combining speech enhancement, robust feature extraction and feature normalization up to a fivefold error rate reduction can be achieved for certain tasks.

In this paper, we propose a noise compensation method for robust speech recognition in DSR (Distributed Speech Recognition) systems based on histogram equalization and correlation information. The objective of this method is to exploit the correlation between components of the feature vector and the temporal correlation between consecutive frames of each component. The recognition experiments, including results in the Aurora 2, Aurora 3-Spanish and Aurora 3-Italian databases, demonstrate that the use of this correlation information increases the recognition accuracy. Index Terms: Distributed Speech Recognition, noise compensation, histogram equalization, correlation information

This paper shows an improved statistical test for voice activity detection in noise adverse environments. The method is based on a revised contextual likelihood ratio test (LRT) defined over a multiple observation window. The motivations for revising the original multiple observation LRT (MO-LRT) are found in its artificially added hangover mechanism that exhibits an incorrect behavior under different signal-to-noise ratio (SNR) conditions. The new approach defines a maximum a posteriori (MAP) statistical test in which all the global hypotheses on the multiple observation window containing up to one speech-to-nonspeech or nonspeech-to-speech transitions are considered. Thus, the implicit hangover mechanism artificially added by the original method was not found in the revised method so its design can be further improved. With these and other innovations, the proposed method showed a higher speech/nonspeech discrimination accuracy over a wide range of SNR conditions when compared to the original MO-LRT voice activity detector (VAD). Experiments conducted on the AURORA databases and tasks showed that the revised method yields significant improvements in speech recognition performance over standardized VADs such as ITU T G.729 and ETSI AMR for discontinuous voice transmission and the ETSI AFE for distributed speech recognition (DSR), as well as over recently reported methods.

This paper shows a complete seismic-event classification and monitoring system that has been developed based on the seismicity observed during three summer Antarctic surveys at the Deception Island Volcano, Antarctica. The system is based on the state of the art in hidden Markov modeling (HMM) techniques successfully applied to other scenarios. A database that contains a representative set of different seismic events including volcano-tectonic earthquakes, long period (LP) events, volcanic tremor, and hybrid events that were recorded during the 1994–1995 and 1995–1996 seismic surveys was collected for training and testing. Simple left-to-right HMMs and multivariate Gaussian probability density functions with a diagonal covariance matrix were used. The feature vector consists of the log-energies of a filter bank that consists of 16 triangular weighting functions that were uniformly spaced between 0 and 20 Hz and the first- and second-order derivatives. The system is suitable to operate in real time, and its accuracy for this task is about 90%. On the other hand, when the system was tested with a different data set including mainly LP events that were registered during several seismic swarms during the 2001–2002 field survey, more than 95% of the recognized events were marked by the recognition system.

This paper shows a complete volcano monitoring system that has been developed on the basis of the seismicity observed during three summer Antarctic surveys at Deception Island Volcano (Antarctica). The system is based on the state of the art in hidden Markov modelling (HMM) techniques successfully applied to other scenarios. A database containing a representative set of different seismic events including volcano-tectonic earthquakes, long-period events, volcanic tremor and hybrid events recorded during the 1994-1995 and 1995-1996 seismic surveys was collected for training and testing. Simple left-to-right HMMs and multivariate Gaussian probability density functions (PDF) with diagonal covariance matrix were used. The feature vector consists of the log-energies of a filter-bank consisting of 16 triangular weighting functions uniformly spaced between 0 and 20 Hz plus the first and second order derivatives. The system is suitable to operate in real-time and its accuracy is close to 90%. When the system was tested with a different data set including mainly long-period events registered during several seismic swarms during the 2001-2002 field survey, more than 95% of the recognized events were correctly marked by the recognition system

A new front-end normalization algorithm that uses a parametric nonlinear transformation is proposed in this paper. The method improves histogram equalization based nonlinear transformations by finding a simple and computationally inexpensive parametric expression of the nonlinear transformation. The new parametric approach relies on a two Gaussian model for the probability distribution of the features, and on a simple Gaussian classifier to label the input frames as belonging to the speech or non-speech classes. The result is a more robust equalization, less dependent on the percentage of speech and non-speech frames. Recognition experiments on the AURORA 4 database have been performed and the effectiveness of the algorithm is analyzed in comparison with other linear and nonlinear feature equalization techniques

This paper shows an effective voice activity detector based on a statistical likelihood ratio test defined on the integrated bispectrum of the signal. It inherits the ability of higher order statistics to detect signals in noise with many other additional advantages: i) its computation as a cross spectrum leads to significant computational savings, and ii) the variance of the estimator is of the same order as that of the power spectrum estimator. The proposed method incorporates contextual information to the decision rule, a strategy that has reported significant improvements in speech detection accuracy and robust speech recognition applications. The experimental analysis conducted on the well-known AURORA databases has reported significant improvements over standardized techniques such as ITU G.729, AMRI, AMR2 and ESTI AFE VADs, as well as over recently published VADs

This paper presents GAIALAB, a Weblab project for digital communications distributed learning developed at the University of Granada (Spain). This initiative has been funded by a University program to improve teaching quality. The project addresses problems of teaching and learning innovatively and effectively by enabling the user interaction with the simulation engines through a Web browser. Our Weblab project provides an interface with JAVA applets that have been developed and integrated in the system. The Weblab project enables simulating digital communication systems and analyzing the results obtained through the Web. Moreover, all the applets that are included in the system have been designed to allow the modification of the simulation parameters, thus enabling a better understanding of the algorithms. The system also permits evaluating the experimental work developed by the students

This paper presents a preliminary study of an optical flow-based parameterization of visual information in a sign language recognition system using Hidden Markov Models (HMM). Current feature extraction processes need initialization, tracking and segmentation stages in order to describe signer gestures. Our aim is to develop a single and fast technique to reduce computational complexity which doesn't require these stages and is able to work in mobile devices with limited hardware resources. The Moving Block Distance (MBD) parameterization is an interesting first approach for this purpose, proved by two signers under a static background constraint. A lexicon of 33 basic word units (signemes) was used to build the data set containing phrases with a variable number of words. Continuous recognition results achieve more than 99 % accuracy in close test. Index Terms: multi-modal recognition, visual feature extraction, mobile devices, sign language, optical flow. 1.

A filter that introduces inter-frame information into the voice features set is proposed in this paper. The filter adds the autocorrelations of the cepstral coefficients to the set of characteristics used for training and recognition. Those autocorrelations should not depend on the environment conditions. Because they should only depend on the information to recognize, a normalization of that inter-frame information is convenient. The filter defined implements this normalization by transforming the autocorrelations into a normalized domain defined with clean adaptation data. This temporal processing of the features is added to the Histogram Equalization of the cepstral coefficients (HEQ) used to normalize the MFCCs. An analysis is done about the most effective domain (original MFCCS or equalized MFCCs) on which the temporal processing should be executed. Performance results for the proposed algorithm are presented for AURORA2 and AURORA4 databases.

Abstract In this paper we propose a novel Voice Activity Detection (VAD)algorithm, based on the integrated bispectrum function (IBI), forimproving Automated Speech Recognition (ASR) systems thatwork in noisy environments. In particular we use the combina-tion of two features, IBI magnitude and IBI phase to formulatea robust and smoothed decision rule for speech/pause discrimina-tion. The analysis performed on the new combined feature high-lighted: i) the advantages of each individual feature, while com-pensating the drawback of each other, and ii) the higher ability forendpoint detection given by a lower variance of the decision func-tion in pause/speech frames. The experiments conducted on theSpanish SpeechDat-Car database showed that the proposed algo-rithm outperforms ITU G.729, ETSI AMR1 and AMR2 and ETSIAFE standards as well as other recently reported VAD methods inspeech/non-speech detection performance.IndexTerms: voiceactivitydetection, clusteringanalysis, bispec-trum function, entropy.

This paper shows an effective speech/non-speech discrimination method for improving the performance of speech processing systems working in noisy environment. The proposed method uses a trained support vector machine (SVM) that defines an optimized non-linear decision rule over different sets of speech features. Two alternative feature extraction processes based on: i) subband SNR estimation after denoising, and ii) long-term SNR estimation were compared. Both methods show the ability of the SVM-based classifier to learn how the signal is masked by the acoustic noise and to define an effective non-linear decision rule. However, it is shown that a feature vector incorporating contextual information yielded better speech/non-speech discrimination even when no denoising is applied. The experimental analysis carried out on the Spanish SpeechDat-Car database shows clear improvements over standard VADs including ITU G.729, ETSI AMR and ETSI AFE for distributed speech recognition (DSR), and other recently reported VADs. Index Terms: voice activity detection, support vector machine learning, speech enhancement.

We propose a model-based VAD derived from the Vector Taylor Series (VTS) approach. A Gaussian mixture (trained with clean speech) is used in order to provide an appropriate decision rule for speech/non-speech detection. Additionally, VTS approach adapts the Gaussian mixture to noise conditions, yielding a stable perfor- mance for a wide range of SNRs. We have evaluated its ability for speech/non-speech detection and also its application for robust speech recognition. When compared to other VAD methods, the proposed VAD shows the best trade-off in speech/non-speech de- tection. When applied for Wiener Filtering and for frame drop- ping, the proposed VAD also provides the best recognition results. Index Terms: voice activity detection (VAD), vector Taylor series approach (VTS), Gaussian mixture, Wiener filtering.

A robust and effective voice activity detection (VAD) algorithm is proposed for improving speech recognition performance in noisy environments. The approach is based on well-known statistical tests based on the determination of the speech/non-speech bispectra by means of third-order auto-cumulants. This algorithm differs from many others in the way the decision rule is formulated being the statistical tests built on a multiple observation (MO) window consisting of averaged bispectrum coefficients of the speech signal. Clear improvements in speech/non-speech discrimination accuracy demonstrate the effectiveness of the proposed VAD. It is shown that application of a statistical detection test leads to a better separation of the speech and noise distributions, thus allowing a more effective discrimination and a tradeoff between complexity and performance. The experimental analysis carried out on the AURORA 3 databases provides an extensive performance evaluation together with an exhaustive comparison to the standard VADs, such as ITU G.729, GSM AMR, and ETSI AFE, for distributed speech recognition (DSR) and other recently reported VADs

This letter shows an effective statistical voice activity detection algorithm based on the integrated bispectrum, which is defined as a cross spectrum between the signal and its square and inherits the ability of higher order statistics to detect signals in noise with many other additional advantages: 1) its computation as a cross spectrum leads to significant computational savings, and 2) the variance of the estimator is of the same order as that of the power spectrum estimator. The decision rule is formulated in terms of an average likelihood ratio test (LRT) involving successive integrated bispectrum speech features. With these and other innovations, the proposed method reports significant improvements in speech/pause discrimination as well as in speech recognition over standardized techniques such as ITU-T G.729, ETSI AMR, and AFE VADs, and over recently published VADs

Currently, there are technology barriers inhibiting speech processing systems that work in extremely noisy conditions from meeting the demands of modern applications. This letter presents a new voice activity detector (VAD) for improving speech detection robustness in noisy environments and the performance of speech recognition systems. The algorithm defines an optimum likelihood ratio test (LRT) involving multiple and independent observations. The so-defined decision rule reports significant improvements in speech/nonspeech discrimination accuracy over existing VAD methods that are defined on a single observation and need empirically tuned hangover mechanisms. The algorithm has an inherent delay that, for several applications, including robust speech recognition, does not represent a serious implementation obstacle. An analysis of the overlap between the distributions of the decision variable shows the improved robustness of the proposed approach by means of a clear reduction of the classification error as the number of observations is increased. The proposed strategy is also compared to different VAD methods, including the G.729, AMR, and AFE standards, as well as recently reported algorithms showing a sustained advantage in speech/nonspeech detection accuracy and speech recognition performance.

An effective voice activity detection (VAD) algorithm is proposed for improving speech recognition performance in noisy environments. The approach is based on the determination of the speech/nonspeech divergence by means of specialized order statistics filters (OSFs) working on the subband log-energies. This algorithm differs from many others in the way the decision rule is formulated. Instead of making the decision based on the current frame, it uses OSFs on the subband log-energies which significantly reduces the error probability when discriminating speech from nonspeech in a noisy signal. Clear improvements in speech/nonspeech discrimination accuracy demonstrate the effectiveness of the proposed VAD. It is shown that an increase of the OSF order leads to a better separation of the speech and noise distributions, thus allowing a more effective discrimination and a tradeoff between complexity and performance. The algorithm also incorporates a noise reduction block working in tandem with the VAD and showed to further improve its accuracy. A previous noise reduction block also improves the accuracy in detecting speech and nonspeech. The experimental analysis carried out on the AURORA databases and tasks provides an extensive performance evaluation together with an exhaustive comparison to the standard VADs such as ITU G.729, GSM AMR, and ETSI AFE for distributed speech recognition (DSR), and other recently reported VADs.

This paper describes a method of compensating for nonlinear distortions in speech representation caused by noise. The method described here is based on the histogram equalization method often used in digital image processing. Histogram equalization is applied to each component of the feature vector in order to improve the robustness of speech recognition systems. The paper describes how the proposed method can be applied to robust speech recognition and it is compared with other compensation techniques. The recognition experiments, including results in the AURORA II framework, demonstrate the effectiveness of histogram equalization when it is applied either alone or in combination with other compensation techniques.

Currently, there are technology barriers inhibiting speech processing systems working under extreme noisy conditions. The emerging applications of speech technology, especially in the fields of wireless communications, digital hearing aids or speech recognition, are some examples of such systems often requiring a noise reduction technique in combination with a precise voice activity detector (VAD). This paper presents a new VAD for improving speech detection robustness in noisy environments and the performance of speech recognition systems. The algorithm uses long-term information about the speech signal to formulate the decision rule and estimates the subband SNR using specialized order statistics filters (OSF). The proposed algorithm is compared to the most commonly used VAD in the field, in terms of speech/nonspeech discrimination and also in terms of recognition performance when the VAD is used in an automatic speech recognition (ASR) system. Experimental results demonstrate a sustained advantage over different VAD methods including standard VAD such as G.729 and AMR which are used as a reference, the VAD of the Advanced Front-End (AFE) for distributed speech recognition (DSR), and recently reported algorithms.

The paper mainly focusses on an improved voice activity detection algorithm employing long-term signal processing and maximum spectral component tracking. The benefits of this approach have been analyzed in a previous work (Ramirez, J. et al., Proc. EUROSPEECH 2003, p.3041-4, 2003) with clear improvements in speech/non-speech discriminability and speech recognition performance in noisy environments. Two clear aspects are now considered. The first one, which improves the performance of the VAD in low noise conditions, considers an adaptive length frame window to track the long-term spectral components. The second one reduces misclassification errors in highly noisy environments by using a noise reduction stage before the long-term spectral tracking. Experimental results show clear improvements over different VAD methods in speech/pause discrimination and speech recognition performance. Particularly, improvements in recognition rate were reported when the proposed VAD replaced the VADs of the ETSI advanced front-end (AFE) for distributed speech recognition (DSR).

Currently, new trends in wireless communications are demanding reliable human-machine interaction in real-life environments. However, there are obstacles inhibiting automatic speech recognition systems (ASR) working in noisy environments. The main difficulty is the degradation suffered by ASR systems due to a mismatch between training and test conditions. This paper shows an improved voice activity detector (VAD) combining noise reduction and subband divergence estimation for improving the reliability of speech recognizers operating in noisy environments. The algorithm formulates the decision rule by measuring the divergence between the subband spectral magnitude of speech and noise using the Kullback-Leibler (KL) distance on the denoised signal. Experiments demonstrate a sustained advantage over different VAD methods including standard VADs such as G.729 and AMR, which are used as a reference, recently reported algorithms, and the VADs of the advanced frontend (AFE) for distributed speech recognition (DSR). 1.

Noise compensation methods for speech recognition provide a cleaned version of the speech representation. Usually this cleaned version is the expected value of the speech parameters given the observed noisy speech and the noise statistic. A more realistic representation should include the probability distribution of the cleaned speech instead of its expected value in order to represent the uncertainty associated to the compensation process due to the variability of the noise process. Recently, the inclusion of the uncertainty in the recognition process has been studied. Some approaches represent the uncertainty in the HMM parameters values. Other approaches represent it in the feature space. This second approach offers a much simpler system implementation and lower computational cost. In this paper we have developed a noise compensation technique that incorporates the variance of the cleaned speech into the speech representation. The variance is estimated using a Wiener filter during the speech feature enhancement process. This way of including the uncertainty implies the modification of the decoding rule. Experimental results using AURORA 2 database demonstrate a sustained improvement of the performance in the recognition system (about 21% word error rate reduction) when uncertainty is considered in the decoding rule.

This letter shows an innovative voice activity detector (VAD) based on the Kullback-Leibler (KL) divergence measure. The algorithm is evaluated in the context of the recently approved ETSI standard for distributed speech recognition (DSR). The VAD uses long-term information of the noisy speech signal in order to define a more robust decision rule yielding high accuracy. The mel-scaled filter bank log-energies (FBE) are modeled by means of Gaussian distributions, and a symmetric KL divergence is used for the estimation of the distance between speech and noise distributions. The decision rule is formulated in terms of the average subband KL divergence that is compared to a noise-adaptable threshold. An exhaustive analysis using the AURORA databases is conducted in order to assess the performance of the proposed method and to compare it to existing standard VAD methods.

This letter presents a new segmental nonlinear feature normalization algorithm to improve the robustness of speech recognition systems against variations of the acoustic environment. An experimental study of the best delay-performance tradeoff is conducted within the AURORA-2 framework, and a comparison with two commonly used normalization algorithms is presented. Computationally efficient algorithms based on order statistics are also presented. One of them is based on linear interpolation between sampling quantiles, and the other one is based on a point estimation of the probability distribution. The reduction in the computational cost does not degrade the performance significantly.

This paper shows an efficient voice activity detector (VAD) that is based on the estimation of the long-term spectral diver- gence (LTSD) between noise and speech periods. The proposed method decomposes the input signal into overlapped speech frames, uses a sliding window to compute the long-term spec- tral envelope and measures the speech/non-speech LTSD, thus yielding a high discriminating decision rule and minimizing the average number of decision errors. In order to increase non- speech detection accuracy, the decision threshold is adapted to the measured noise energy while a controlled hang-over is ac- tivated only when the observed signal-to-noise ratio (SNR) is low. An exhaustive analysis of the proposed VAD is carried out using the AURORA TIdigits and SpeechDat-Car (SDC) databases. The proposed VAD is compared to the most com- monly used ones in the field in terms of speech/non-speech detection and recognition performance. Experimental results demonstrate a sustained advantage over G.729, AMR and AFE VADs.

This paper is mainly focused on showing experimental results of a feature extraction algorithm that combines spectral noise reduction and nonlinear feature normalization. The successfulness of this approach has been shown in a previous work, and in this one, we present several improvements that result in a performance comparable to that of the recently approved AFE for DSR. Noise reduction is now based on a Wiener filter instead of spectral subtraction. The voice activity detection based on the full-band energy has been replaced with a new one using spectral information. Relative improvements of 24.81% and 17.50% over our previous system are obtained for AURORA 2 and 3 respectively. Results for AURORA 2 are not as good as those for the AFE, but for AURORA 3 a relative improvement of 5.27% is obtained.

The VTS approach for noise reduction is based on a statistical for mulation. It provides the expected value of the clean speech given the noisy observations and statistical models for the clean speech and the additive noise. The compensated signal is only an approximation of the clean one and retains a residual mismatch. The main objective of this work is to characterize this residual noise and to propose techniques to reduce its unwanted effects. Two different approaches to this problem are presented in this paper. The first one is based on linear filtering the time sequences of compensated acoustic parameters; for this purpose we use LDA-based RASTA-like FIR filters. The second approach is based on canceling the distortion introduced into the probability distribution of acoustic parameters and uses the well-known technique of histogram equalization. Results reported on AURORA database show that the proposed methods increase the recognition performance.

The noise usually produces a non-linear distortion of the feature space considered for Automatic Speech Recognition. This distortion causes a mismatch between the training and recognition conditions which significantly degrades the performance of speech recognizers. In this contribution we analyze the effect of the additive noise over cepstral based representations and we compare several approaches to compensate this effect. We discuss the importance of the non-linearities introduced by the noise and we propose a method (based on the histogram equalization technique) specifically oriented to the compensation of the non-linear transformation caused by the additive noise. The proposed method has been evaluated using the AURORA-2 database and task. The recognition results show significant improvements with respect to other compensation methods reported in the bibliography and reveals the importance of the non-linear effects of the noise and the utility of the proposed method.

This paper presents a new method for testing dialogue systems using a variety of real-world conditions simulated in lab. The method is based on the use of an additional dialogue system, called simulator, designed to behave as users interacting with the dialogue system to test. The behavior of the simulator is decided from diverse scenarios that represent user goals. The simulator tries to achieve the goals in the scenarios during the interaction with the dialogue system. We applied the method to test in noise conditions a dialogue system under development in our lab, considering white and babble noise. The method allowed to find out errors in the recognizer and in the strategy the system uses to handle user confirmations. Using the method, we tested the behavior of the system in terms of task completion, taking into account a VTS noise compensation technique. The experiments show that, if the VTS technique is not used, the average task completion is 31,43% for the white noise and 29,64% for the babble noise. If the VTS technique is used, the average task completion increases to 57,35% for the white noise and to 54,44% for the babble noise.

Distributed Speech Recognition involves the development of techniques to mitigate the degradations that the transmission channel introduces in the speech features. This work proposes an HMM framework from which different mitigation techniques oriented to bursty channels can be derived. In particular, two MMSE-based and a new Viterbi-based mitigation procedures are derived under this framework. Several implementation issues such as the channel SNR estimation or the application of hard decision on the received signal vectors are dealt with. Also, different boundary conditions suitable for the speech recognition application are studied for the different mitigation procedures. The experimental results show that the HMM-based techniques can effectively mitigate channel errors, even in very poor channel conditions.

This work is mainly focused on showing experimental results using a combination of two methods for noise compensation which are shown to be complementary: classical spectral subtraction algorithm and histogram equalization. While spectral subtraction is focused on the reduction of the additive noise in the spectral domain, histogram equalization is applied in the cepstral domain to compensate the remaining non-linear effects associated to channel distortion and additive noise. The estimation of the noise spectrum for the spectral subtraction method relies on a new algorithm for speech / non-speech detection (SND) based on order statistics. This SND classification is also used for dropping long speech pauses. Results on Aurora 2 and Aurora 3 are reported.

This paper presents a novel technique that allows testing spoken dialogue systems by means of an automatic generation of conversations. The technique permits to easily test spoken dialogue systems under a variety of lab-simulated conditions, as it is easy to vary or change the utterance corpus used to check the performance of the system. The technique is based on the use of a module called user simulator whose purpose is to behave as real users when they interact with dialogue systems. The behaviour of the simulator is decided by means of diverse scenarios that represent the goals of the users. The simulator aim is to achieve the goals set in the scenarios during the interaction with the dialogue system. We have applied the technique to test a dialogue system developed in our lab. The test has been carried out considering different levels of white and babble noise as well as a VTS noise compensation technique. The results prove that the dialogue system performance is worse under the babble noise conditions. The VTS technique has been effective when dealing with noisy utterances and has lead to better experimental results, particularly for the white noise. The technique has permitted to detect problems in the dialogue strategies employed to handle confirmation turns and recognition errors, suggesting that these strategies must be improved.

Recently, the first version of an ETSI standard for Distributed Speech Recognition has been proposed. The main benefit of this approach is the possibility of maintaining a high recognition performance when accessing remote information systems. The use of a digital channel for transmission of the encoded speech parameters implies the introduction of several channel distortions. Our paper deals with the mitigation of such distortions. We study the application of MMSE estimation to this problem and propose a new MMSE procedure that obtains the probabilities needed for MMSE from a forward-backward algorithm. We show that MMSE estimation obtains better performance than the mitigation algorithm described in the ETSI standard under different channel conditions.

In this paper we present a study about time-frequency distribution of acoustic-phonetic information for the Spanish language. This is based on a large Spanish database automatically labeled, and we conclude that results are similar to those obtained for hand-labeled english databases. We use bidimensional LDA [1] to extract discriminant features in time-frequency domain (TF) that are more robust in noise than the standard ones based on MFCC and time derivatives. We show that TF domain and its corresponding transformed domain (CTM) are equivalent from the point of view of LDA analysis and use this fact to reduce the dimensionality of the problem. Finally, cascade unidimensional LDA (CLDA) is applied first in frequency and then in time. This gives better estimates of projection vectors and better recognition performance. The proposed techniques are evaluated in a connected digit recognition task. Utterances have been artificially corrupted with additive real noises.

In this paper we apply a model-based compensation method to cancel the effect of the additive noise in Automatic Speech Recognition systems. The method is formulated in a statistical framework in order to perform the optimal compensation of the noise effect given the observed noisy speech, a model describing the statistics of the speech recorded in a clean reference environment and the estimation of the noise in the noisy recognition environment. The noise is estimated using the first frames of the sentence to be recognized and a frame-by-frame noise compensation algorithm is performed, so that the compensation procedure does not constrain real-time speech recognition systems and is compatible with emerging technologies based on distributed speech recognition.

This article describes a comparative study of language models in which the evaluation protocol has been set by AUPELF-UREF . We especially pay attention on the comparison between two methods of clustering words which are necessary in the design of the corresponding language models. The first classification is done by following a linguistic and theoretical method and the second one is based on an optimization method. Both methods are evaluated through the Shannon game. The vocabulary used is 20 000 words, the training corpus is made of two years of Le Monde newspaper (42M of words) and the test corpus (400 000 words) is extracted from 6 years of Le Monde Diplomatique. First evaluations show an improvement of 13% of recognized words in the first five ranks and a decrease of 25% in perplexity.This article describes a comparative study of language models in which the evaluation protocol has been set by AUPELF-UREF . We especially pay attention on the comparison between two methods of clustering words which are necessary in the design of the corresponding language models. The first classification is done by following a linguistic and theoretical method and the second one is based on an optimization method. Both methods are evaluated through the Shannon game. The vocabulary used is 20 000 words, the training corpus is made of two years of Le Monde newspaper (42M of words) and the test corpus (400 000 words) is extracted from 6 years of Le Monde Diplomatique. First evaluations show an improvement of 13% of recognized words in the first five ranks and a decrease of 25% in perplexity.This article describes a comparative study of language models in which the evaluation protocol has been set by AUPELF-UREF . We especially pay attention on the comparison between two methods of clustering words which are necessary in the design of the corresponding language models. The first classification is done by following a linguistic and theoretical method and the second one is based on an optimization method. Both methods are evaluated through the Shannon game. The vocabulary used is 20 000 words, the training corpus is made of two years of Le Monde newspaper (42M of words) and the test corpus (400 000 words) is extracted from 6 years of Le Monde Diplomatique. First evaluations show an improvement of 13% of recognized words in the first five ranks and a decrease of 25% in perplexity.This article describes a comparative study of language models in which the evaluation protocol has been set by AUPELF-UREF . We especially pay attention on the comparison between two methods of clustering words which are necessary in the design of the corresponding language models. The first classification is done by following a linguistic and theoretical method and the second one is based on an optimization method. Both methods are evaluated through the Shannon game. The vocabulary used is 20 000 words, the training corpus is made of two years of Le Monde newspaper (42M of words) and the test corpus (400 000 words) is extracted from 6 years of Le Monde Diplomatique. First evaluations show an improvement of 13% of recognized words in the first five ranks and a decrease of 25% in perplexity.

It is usually assumed that grammar probabilities and acoustic probabilities in a continuous speech recognition system have to be incorporated to the general score with different weights. This is an experimental fact and there is no generally accepted theoretical explanation. We propose an explanation to this fact, related to the way grammar scoring is incorporated in the searching procedure. Accordingly to this explanation, we perform a set of experiments to test our hypothesis. We are also proposing a new way of introducing grammar probabilities in a tree-based vocabulary search strategy, where systems are usually bound to use the worst strategy. To apply our ideas to unigrams is rather simple. For more complex language models like bigrams we have to implement a new procedure.

This work presents the STACC, Sistema Telef onico Autom atico de Consulta de Calificaciones (Automatic Telephone System for Consulting Marks). This system has been developed at our laboratory during 1996 and implements a service through telephone line that allows the students to consult by speech their marks after the exams by means of a simple phone call. This experience provided us an interesting point of view about the problems of real applications of speech technology. In this work we describe the system and some statistics about the use of STACC by the students are presented.

Research on multiple vector quantization (MVQ) has shown the suitability of such a technique for speech recognition. Basically, MVQ proposes the use of one separate VQ codebook for each recognition unit. Thus, a MVQHMM model is composed of a VQ codebook and a discrete HMM model. This technique allows the incorporation in the recognition dynamics of the input sequence information wasted by discrete HMM models in the VQ process. The use of distinct codebooks also allows one to train them in a discriminative manner. We propose a new VQ codebook design method for MVQ-based systems, obtained from a modified maximum mutual information estimation. This method provides meaningful error reductions and is performed independently from the estimation of the discrete HMM part of the MVQ model. The results show that the proposed discriminative design turns the MVQHMM technique into a powerful acoustic modeling tool in comparison with other classical methods such as discrete or semicontinuous HMMs.

Although the continuous HMM (CHMM) technique seems to be the most flexible and complete tool for speech modeling, it is not always used for the implementation of speech recognition systems due to several problems related to training and computational complexity. Besides, it is not clear the superiority of continuous models over other well-known types of HMMs, such as discrete (DHMM) or semicontinuous (SCHMM) models, or multiple vector quantization (MVQ) models, a new type of HMM modeling. The authors propose a new variant of HMM models, the SCMVQ, HMM models (semicontinuous multiple vector quantization HMM), that uses one VQ codebook per recognition unit and several quantization candidates, Formally, SCMVQ modeling is the closest one to CHMM, although requiring less computation than SCHMMs. Besides, the authors show that SCMVQs can obtain better recognition results than DHMMs, SCHMMs or MVQs.>

This paper presents a new framework developed to apply Alphanets to CSR. For this purpose, a modular system is proposed. This system is made up by three different modules: LVQ module, SLHMM module and DP module. The SLHMM module is an expansion of an Alphanet, and therefore, can be interpreted as a HMM. The system can be trained globally applying backpropagation techniques. The used pruning procedure is based upon recognized units instead of observations, which reduces the number of nodes needed to recognize a sentence, compared to HMM-based systems using the same parameters for the models in both systems. Besides, the training procedure re-adapts the weights according to the new architecture in a few iterations since the initial parameters can be estimated from a classical HMM CSR system.>