Grapheme-based Automatic Speech Recognition using KL-HMM

The state-of-the-art automatic speech recognition (ASR) systems typically use phonemes as subword units. In this work, we present a novel grapheme-based ASR system that jointly models phoneme and grapheme information using Kullback-Leibler divergence-based HMM system (KL-HMM). More specifically, the underlying subword unit models are grapheme units and the phonetic information is captured through phoneme posterior probabilities (referred as posterior features) estimated using a multilayer perceptron (MLP). We investigate the proposed approach for ASR on English language, where the correspondence between phoneme and grapheme is weak. In particular, we investigate the effect of contextual modeling on grapheme-based KL-HMM system and the use of MLP trained on auxiliary data. Experiments on DARPA Resource Management corpus have shown that the grapheme-based ASR system modeling longer subword unit context can achieve same performance as phoneme-based ASR system, irrespective of the data on which MLP is trained.

Boosting Localized Features for Speaker and Speech Recognition

Anindya Roy

In this thesis, we propose a novel approach for speaker and speech recognition involving localized, binary, data-driven features. The proposed approach is largely inspired by similar localized approaches in the computer vision domain. The success of these existing approaches coupled with their proven advantages of robustness and computational efficiency motivated us to apply these ideas to the speech domain. Our approach is distinct from the standard cepstral features-based approach for speaker and speech recognition.% which models the envelope of short-time spectrum of speech and could be termed as holistic. The proposed approach starts with a large set of simple localized features, each of which looks at very small parts of spectro-temporal representations of speech. Each feature is binary-valued. The most discriminative of these features are selected by boosting and combined to form the final classifier. Two systems are developed based on this general framework, a speaker recognition system and a speech recognition system. The speaker recognition system is evaluated under a wide range of experimental conditions, using clean speech, noisy speech and speech data collected from mobile phones. The system performs reliably in each condition, comparable with the standard systems using cepstral features and Gaussian Mixture Models. At the same time, it involves significantly lower number of floating point operations compared to these systems. In the case of the speech recognition system, we integrate our localized features with a Hidden Markov Model framework using multilayer perceptrons. Continuous speech recognition studies on standard databases show that these features perform equally well as cepstral features. It is also found that the fusion of these features with cepstral features leads to improved performance at both the feature level and the decision level. Apart from this, minor contributions include an audio-visual person recognition system developed using the same general approach of localized features described above, extending its applicability. Finally, a new (but related) class of localized features was developed for robust face detection.

Ecole Polytechnique Federale de Lausanne (EPFL)2011

Boosting Localized Features for Speaker and Speech Recognition

Anindya Roy

In this thesis, we propose a novel approach for speaker and speech recognition involving localized, binary, data-driven features. The proposed approach is largely inspired by similar localized approaches in the computer vision domain. The success of these existing approaches coupled with their proven advantages of robustness and computational efficiency motivated us to apply these ideas to the speech domain. Our approach is distinct from the standard cepstral features-based approach for speaker and speech recognition. The proposed approach starts with a large set of simple localized features, each of which looks at very small parts of spectro-temporal representations of speech. Each feature is binary-valued. The most discriminative of these features are selected by boosting and combined to form the final classifier. Two systems are developed based on this general framework, a speaker recognition system and a speech recognition system. The speaker recognition system is evaluated under a wide range of experimental conditions, using clean speech, noisy speech and speech data collected from mobile phones. The system performs reliably in each condition, comparable with the standard systems using cepstral features and Gaussian Mixture Models. At the same time, it involves significantly lower number of floating point operations compared to these systems. In the case of the speech recognition system, we integrate our localized features with a Hidden Markov Model framework using multilayer perceptrons. Continuous speech recognition studies on standard databases show that these features perform equally well as cepstral features. It is also found that the fusion of these features with cepstral features leads to improved performance at both the feature level and the decision level. Apart from this, minor contributions include an audio-visual person recognition system developed using the same general approach of localized features described above, extending its applicability. Finally, a new (but related) class of localized features was developed for robust face detection.

EPFL2011

Multilingual Training and Adaptation in Speech Recognition

Sibo Tong

State-of-the-art acoustic models for Automatic Speech Recognition (ASR) are based on Hidden Markov Models (HMM) and Deep Neural Networks (DNN) and often require thousands of hours of transcribed speech data during training. Therefore, building multilingual ASR systems or systems on a language with few resources is a challenging task. Multilingual training and cross-lingual adaptation are potential solutions. However, context-dependent states modeling creates difficulties for multilingual and cross-lingual ASR because of the large increase in context dependent labels arising from the phone set mismatch. The goal of this thesis is to improve current state-of-the-art acoustic modeling techniques in general for ASR, with a particular focus on multilingual ASR and cross-lingual adaptation. We systematically exploited new training frameworks, from Maximum Likelihood Estimation, Connectionist Temporal Classification to Maximum Mutual Information, in the context of phoneme-based multilingual training. In order to minimize the negative effects of data impurity arising from language mismatch, we investigated language adaptive training approaches which help further improve the multilingual ASR performance. Through comprehensive experimental comparison we demonstrated that phoneme-based multilingual models are easily extensible to unseen phonemes of new languages, from which the cross-lingual adaptation yields significant improvement over traditional approaches on limited data. Finally, we proposed a semi-supervised training approach based on dropout to boost the performance in low-resourced languages using untranscribed data. In the other part of the thesis, we conducted more theoretical analysis of techniques found to be useful in sequential multilingual training. More specifically, we revisited the recurrent architecture based on Bayesâs theorem. This leads to a Bayesian recurrent unit dictated by the probabilistic formulation and naturally support a backward recursion. Experiments show that the proposed architecture exceeds the performance of conventional recurrent network. Together, this thesis constitutes a thorough analysis of the current field. Through theoretical and experimental comparisons, the proposed approaches are shown to yield significant improvement over the conventional hybrid systems on multilingual speech recognition.

EPFL2020

Multilingual Training and Adaptation in Speech Recognition

Sibo Tong

EPFL2020

Boosting Localized Features for Speaker and Speech Recognition

Anindya Roy

Ecole Polytechnique Federale de Lausanne (EPFL)2011

Boosting Localized Features for Speaker and Speech Recognition

Anindya Roy

In this thesis, we propose a novel approach for speaker and speech recognition involving localized, binary, data-driven features. The proposed approach is largely inspired by similar localized approaches in the computer vision domain. The success of these existing approaches coupled with their proven advantages of robustness and computational efficiency motivated us to apply these ideas to the speech domain. Our approach is distinct from the standard cepstral features-based approach for speaker and speech recognition. The proposed approach starts with a large set of simple localized features, each of which looks at very small parts of spectro-temporal representations of speech. Each feature is binary-valued. The most discriminative of these features are selected by boosting and combined to form the final classifier. Two systems are developed based on this general framework, a speaker recognition system and a speech recognition system. The speaker recognition system is evaluated under a wide range of experimental conditions, using clean speech, noisy speech and speech data collected from mobile phones. The system performs reliably in each condition, comparable with the standard systems using cepstral features and Gaussian Mixture Models. At the same time, it involves significantly lower number of floating point operations compared to these systems. In the case of the speech recognition system, we integrate our localized features with a Hidden Markov Model framework using multilayer perceptrons. Continuous speech recognition studies on standard databases show that these features perform equally well as cepstral features. It is also found that the fusion of these features with cepstral features leads to improved performance at both the feature level and the decision level. Apart from this, minor contributions include an audio-visual person recognition system developed using the same general approach of localized features described above, extending its applicability. Finally, a new (but related) class of localized features was developed for robust face detection.

EPFL2011