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Automatic Speech Recognition using Dynamic Bayesian Networks with the Energy as an Auxiliary Variable

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Speaker Normalization using HMM2

Hervé Bourlard, Katrin Weber, Shajith Ikbal

In this paper, we present an HMM2 based method for speaker normalization. Introduced as an extension of Hidden Markov Model (HMM), HMM2 differentiates itself from the regular HMM in terms of the emission density modeling, which is done by a set of state-de ...

2002

Mixed Bayesian Networks with Auxiliary Variables for Automatic Speech Recognition

Hervé Bourlard

Standard hidden Markov models (HMMs), as used in automatic speech recognition (ASR), calculate their emission probabilities by an artificial neural network (ANN) or a Gaussian distribution conditioned on the hidden state variable, considering the emissions ...

2002

Modelling auxiliary information (pitch frequency) in hybrid HMM/ANN based ASR systems

Hervé Bourlard

Automatic Speech Recognition systems typically use smoothed spectral features as acoustic observations. In recent studies, it has been shown that complementing these standard features with auxiliary information could improve the performance of the system. ...

IDIAP2002

IDIAP HMM/HMM2 System: Theoretical Basis and Software Specifications

Hervé Bourlard, Samy Bengio, Katrin Weber, Shajith Ikbal

State-of-the-art Automatic Speech Recognition (ASR) systems make extensive use of Hidden Markov Models (HMMs), characterized by flexible statistical modeling, powerful optimization (training) techniques and efficient recognition algorithms. When allowed by ...

IDIAP2001

EPFL lab session 2/2: Introduction to Hidden Markov Models

Sacha Krstulovic

Lab sessions given in relation to Herve Bourlard's Speech Recognition course at EPFL (Ecole Polytechnique Federale de Lausanne), second semester 2001. The full session is available from the web as ftp://ftp.idiap.ch/pub/sacha/labs/Session2.tgz . ...

IDIAP2001

Speech Recognition Using Advanced HMM2 Features

Hervé Bourlard, Samy Bengio, Katrin Weber

HMM2 is a particular hidden Markov model where state emission probabilities of the temporal (primary) HMM are modeled through (secondary) state-dependent frequency-based HMMs [12]. As shown in [13], a secondary HMM can also be used to extract robust ASR fe ...

IDIAP2001

Speech Recognition Using Advanced HMM2 Features

Hervé Bourlard, Samy Bengio, Katrin Weber

2001

Robust Speech Recognition and Feature Extraction Using HMM2

Hervé Bourlard, Samy Bengio, Katrin Weber, Shajith Ikbal

This paper presents the theoretical basis and preliminary experimental results of a new HMM model, referred to as HMM2, which can be considered as a mixture of HMMs. In this new model, the emission probabilities of the temporal (primary) HMM are estimated ...

IDIAP2001

Modeling Auxiliary Information in Bayesian Network Based ASR

Hervé Bourlard

Automatic speech recognition bases its models on the acoustic features derived from the speech signal. Some have investigated replacing or supplementing these features with information that can not be precisely measured (articulator positions, pitch, gende ...

IDIAP2001

Modeling Auxiliary Information in Bayesian Network Based ASR

Hervé Bourlard

2001