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INTEGRATING ARTICULATORY FEATURES USING KULLBACK-LEIBLER DIVERGENCE BASED ACOUSTIC MODEL FOR PHONEME RECOGNITION

Publications associées (37)

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Volterra Series for Analyzing MLP based Phoneme Posterior Probability Estimator

Hynek Hermansky, Joel Praveen Pinto

We present a framework to apply Volterra series to analyze multilayered perceptrons trained to estimate the posterior probabilities of phonemes in automatic speech recognition. The identified Volterra kernels reveal the spectro-temporal patterns that are l ...

Idiap2008

Comparing Different Word Lattice Rescoring Approaches Towards Keyword Spotting

Hervé Bourlard, Hynek Hermansky, Joel Praveen Pinto

In this paper, we further investigate the large vocabulary continuous speech recognition approach to keyword spotting. Given a speech utterance, recognition is performed to obtain a word lattice. The posterior probability of keyword hypotheses in the latti ...

IDIAP2007

Developing and Enhancing Posterior Based Speech Recognition Systems

Hervé Bourlard, Samy Bengio, Hamed Ketabdar

Local state or phone posterior probabilities are often investigated as local scores (e.g., hybrid HMM/ANN systems) or as transformed acoustic features (e.g., ``Tandem'') to improve speech recogni tion systems. In this paper, we present initial results towa ...

2005

Developing and Enhancing Posterior Based Speech Recognition Systems

Hervé Bourlard, Samy Bengio, Hamed Ketabdar

IDIAP2005

Robust audio segmentation

Jitendra Ajmera

Audio segmentation, in general, is the task of segmenting a continuous audio stream in terms of acoustically homogenous regions, where the rule of homogeneity depends on the task. This thesis aims at developing and investigating efficient, robust and unsup ...

EPFL2005

Towards using hierarchical posteriors for flexible automatic speech recognition systems

Hervé Bourlard, Samy Bengio, Bertrand Mesot

Local state (or phone) posterior probabilities are often investigated as local classifiers (e.g., hybrid HMM/ANN systems) or as transformed acoustic features (e.g., ``Tandem'') towards improved speech recognition systems. In this paper, we present initial ...

IDIAP2004

Robust Audio Segmentation

Hervé Bourlard, Jitendra Ajmera

IDIAP2004

Robust Audio Segmentation

Hervé Bourlard, Jitendra Ajmera

École Polytechnique Fédérale de Lausanne2004

Modelling Auxiliary Features in Tandem Systems

Hervé Bourlard, Shajith Ikbal

Tandem systems transform the cepstral features into posterior probabilities of subword units using artificial neural networks (ANNs), which are processed to form input features for conventional speech recognition systems. They have been shown to perform be ...

2004

Modelling Auxiliary Features in Tandem Systems

Hervé Bourlard, Shajith Ikbal

IDIAP2004