Nonparametric uncertainty estimation and propagation for noise robust ASR

International audienceWe consider the framework of uncertainty propagation for automatic speech recognition (ASR) in highly non-stationary noise environments. Uncertainty is considered as the variance of speech distortion. Yet, its accurate estimation in the spectral domain and its propagation to the feature domain remain difficult. Existing methods typically rely on a single uncertainty estimator and propagator fixed by mathematical approximation. In this paper, we propose a new paradigm where we seek to learn more powerful mappings to predict uncertainty from data.We investigate two such possible mappings: linear fusion of multiple uncertainty estimators/propagators and nonparametric uncertainty estimation/propagation. In addition, a procedure to propagate the estimated spectral-domain uncertainty to the static Mel frequency cepstral coefficients (MFCCs), to the log-energy, and to their first- and second-order time derivatives is proposed. This results in a full uncertainty covariance matrix over both static and dynamic MFCCs. Experimental evaluation on Tracks 1 and 2 of the 2nd CHiME Challenge resulted in up to 29% and 28% relative keyword error rate reduction with respect to speech enhancement alone

Noise-Robust Speech Recognition Using Deep Neural Network

Ph.DDOCTOR OF PHILOSOPH

Speech Recognition

Chapters in the first part of the book cover all the essential speech processing techniques for building robust, automatic speech recognition systems: the representation for speech signals and the methods for speech-features extraction, acoustic and language modeling, efficient algorithms for searching the hypothesis space, and multimodal approaches to speech recognition. The last part of the book is devoted to other speech processing applications that can use the information from automatic speech recognition for speaker identification and tracking, for prosody modeling in emotion-detection systems and in other speech processing applications that are able to operate in real-world environments, like mobile communication services and smart homes

Variational Bayesian Inference for Source Separation and Robust Feature Extraction

International audienceWe consider the task of separating and classifying individual sound sources mixed together. The main challenge is to achieve robust classification despite residual distortion of the separated source signals. A promising paradigm is to estimate the uncertainty about the separated source signals and to propagate it through the subsequent feature extraction and classification stages. We argue that variational Bayesian (VB) inference offers a mathematically rigorous way of deriving uncertainty estimators, which contrasts with state-of-the-art estimators based on heuristics or on maximum likelihood (ML) estimation. We propose a general VB source separation algorithm, which makes it possible to jointly exploit spatial and spectral models of the sources. This algorithm achieves 6% and 5% relative error reduction compared to ML uncertainty estimation on the CHiME noise-robust speaker identification and speech recognition benchmarks, respectively, and it opens the way for more complex VB approximations of uncertainty.Dans cet article, nous considérons le problème de l'extraction des descripteurs de chaque source dans un enregistrement audio multi-sources à l'aide d'un algorithme général de séparation de sources. La difficulté consiste à estimer l'incertitude sur les sources et à la propager aux descripteurs, afin de les estimer de façon robuste en dépit des erreurs de séparation. Les méthodes de l'état de l'art estiment l'incertitude de façon heuristique, tandis que nous proposons d'intégrer sur les paramètres de l'algorithme de séparation de sources. Nous décrivons dans ce but une méthode d'inférence variationnelle bayésienne pour l'estimation de la distribution a posteriori des sources et nous calculons ensuite l'espérance des descripteurs par propagation de l'incertitude selon la méthode d'identification des moments. Nous évaluons la précision des descripteurs en terme d'erreur quadratique moyenne et conduisons des expériences de reconnaissance du locuteur afin d'observer la performance qui en découle pour un problème réel. Dans les deux cas, la méthode proposée donne les meilleurs résultats

Automatic speech recognition: from study to practice

Today, automatic speech recognition (ASR) is widely used for different purposes such as robotics, multimedia, medical and industrial application. Although many researches have been performed in this field in the past decades, there is still a lot of room to work. In order to start working in this area, complete knowledge of ASR systems as well as their weak points and problems is inevitable. Besides that, practical experience improves the theoretical knowledge understanding in a reliable way. Regarding to these facts, in this master thesis, we have first reviewed the principal structure of the standard HMM-based ASR systems from technical point of view. This includes, feature extraction, acoustic modeling, language modeling and decoding. Then, the most significant challenging points in ASR systems is discussed. These challenging points address different internal components characteristics or external agents which affect the ASR systems performance. Furthermore, we have implemented a Spanish language recognizer using HTK toolkit. Finally, two open research lines according to the studies of different sources in the field of ASR has been suggested for future work

Mask-based enhancement of very noisy speech

When speech is contaminated by high levels of additive noise, both its perceptual quality and its intelligibility are reduced. Studies show that conventional approaches to speech enhancement are able to improve quality but not intelligibility. However, in recent years, algorithms that estimate a time-frequency mask from noisy speech using a supervised machine learning approach and then apply this mask to the noisy speech have been shown to be capable of improving intelligibility. The most direct way of measuring intelligibility is to carry out listening tests with human test subjects. However, in situations where listening tests are impractical and where some additional uncertainty in the results is permissible, for example during the development phase of a speech enhancer, intrusive intelligibility metrics can provide an alternative to listening tests. This thesis begins by outlining a new intrusive intelligibility metric, WSTOI, that is a development of the existing STOI metric. WSTOI improves STOI by weighting the intelligibility contributions of different time-frequency regions with an estimate of their intelligibility content. The prediction accuracies of WSTOI and STOI are compared for a range of noises and noise suppression algorithms and it is found that WSTOI outperforms STOI in all tested conditions. The thesis then investigates the best choice of mask-estimation algorithm, target mask, and method of applying the estimated mask. A new target mask, the HSWOBM, is proposed that optimises a modified version of WSTOI with a higher frequency resolution. The HSWOBM is optimised for a stochastic noise signal to encourage a mask estimator trained on the HSWOBM to generalise better to unseen noise conditions. A high frequency resolution version of WSTOI is optimised as this gives improvements in predicted quality compared with optimising WSTOI. Of the tested approaches to target mask estimation, the best-performing approach uses a feed-forward neural network with a loss function based on WSTOI. The best-performing feature set is based on the gains produced by a classical speech enhancer and an estimate of the local voiced-speech-plus-noise to noise ratio in different time-frequency regions, which is obtained with the aid of a pitch estimator. When the estimated target mask is applied in the conventional way, by multiplying the speech by the mask in the time-frequency domain, it can result in speech with very poor perceptual quality. The final chapter of this thesis therefore investigates alternative approaches to applying the estimated mask to the noisy speech, in order to improve both intelligibility and quality. An approach is developed that uses the mask to supply prior information about the speech presence probability to a classical speech enhancer that minimises the expected squared error in the log spectral amplitudes. The proposed end-to-end enhancer outperforms existing algorithms in terms of predicted quality and intelligibility for most noise types.Open Acces

Binaural scene analysis : localization, detection and recognition of speakers in complex acoustic scenes

The human auditory system has the striking ability to robustly localize and recognize a specific target source in complex acoustic environments while ignoring interfering sources. Surprisingly, this remarkable capability, which is referred to as auditory scene analysis, is achieved by only analyzing the waveforms reaching the two ears. Computers, however, are presently not able to compete with the performance achieved by the human auditory system, even in the restricted paradigm of confronting a computer algorithm based on binaural signals with a highly constrained version of auditory scene analysis, such as localizing a sound source in a reverberant environment or recognizing a speaker in the presence of interfering noise. In particular, the problem of focusing on an individual speech source in the presence of competing speakers, termed the cocktail party problem, has been proven to be extremely challenging for computer algorithms. The primary objective of this thesis is the development of a binaural scene analyzer that is able to jointly localize, detect and recognize multiple speech sources in the presence of reverberation and interfering noise. The processing of the proposed system is divided into three main stages: localization stage, detection of speech sources, and recognition of speaker identities. The only information that is assumed to be known a priori is the number of target speech sources that are present in the acoustic mixture. Furthermore, the aim of this work is to reduce the performance gap between humans and machines by improving the performance of the individual building blocks of the binaural scene analyzer. First, a binaural front-end inspired by auditory processing is designed to robustly determine the azimuth of multiple, simultaneously active sound sources in the presence of reverberation. The localization model builds on the supervised learning of azimuthdependent binaural cues, namely interaural time and level differences. Multi-conditional training is performed to incorporate the uncertainty of these binaural cues resulting from reverberation and the presence of competing sound sources. Second, a speech detection module that exploits the distinct spectral characteristics of speech and noise signals is developed to automatically select azimuthal positions that are likely to correspond to speech sources. Due to the established link between the localization stage and the recognition stage, which is realized by the speech detection module, the proposed binaural scene analyzer is able to selectively focus on a predefined number of speech sources that are positioned at unknown spatial locations, while ignoring interfering noise sources emerging from other spatial directions. Third, the speaker identities of all detected speech sources are recognized in the final stage of the model. To reduce the impact of environmental noise on the speaker recognition performance, a missing data classifier is combined with the adaptation of speaker models using a universal background model. This combination is particularly beneficial in nonstationary background noise