Publications related to HMM inference towards flexible speech recognition

Acoustic Models for Posterior Features in Speech Recognition

In this thesis, we investigate the use of posterior probabilities of sub-word units directly as input features for automatic speech recognition (ASR). These posteriors, estimated from data-driven methods, display some favourable properties such as increase ...

Idiap2008

A Bayesian Switching Linear Dynamical System for Scale-Invariant robust speech extraction

Bertrand Mesot

Most state-of-the-art automatic speech recognition (ASR) systems deal with noise in the environment by extracting noise robust features which are subsequently modelled by a Hidden Markov Model (HMM). A limitation of this feature-based approach is that the ...

IDIAP2007

On Variable-Scale Piecewise Stationary Spectral Analysis of Speech Signals for ASR

Hervé Bourlard, Vivek Tyagi

It is often acknowledged that speech signals contain short-term and long-term temporal properties that are difficult to capture and model by using the usual fixed scale (typically 20ms) short time spectral analysis used in hidden Markov models (HMMs), base ...

Elsevier2006

Analysis and Classification of EEG Signals using Probabilistic Models for Brain Computer Interfaces

Silvia Chiappa

This thesis explores latent-variable probabilistic models for the analysis and classification of electroenchephalographic (EEG) signals used in Brain Computer Interface (BCI) systems. The first part of the thesis focuses on the use of probabilistic methods ...

IDIAP2006

Analysis and classification of EEG signals using probabilistic models for brain computer interfaces

Silvia Chiappa

This thesis explores latent-variable probabilistic models for the analysis and classification of electroenchephalographic (EEG) signals used in Brain Computer Interface (BCI) systems. The first part of the thesis focuses on the use of probabilistic methods ...

EPFL2006

On Variable-Scale Piecewise Stationary Spectral Analysis of Speech Signals for ASR

Hervé Bourlard, Vivek Tyagi

It is often acknowledged that speech signals contain short-term and long-term temporal properties that are difficult to capture and model by using the usual fixed scale (typically 20ms) short time spectral analysis used in hidden Markov models (HMMs), base ...

IDIAP2005

Efficient integration of automated speech recognition in the framework of dialogue-based vocal systems

In this work, we propose different strategies for efficiently integrating an automated speech recognition module in the framework of a dialogue-based vocal system. The aim is the study of different ways leading to the improvement of the quality and robustn ...

EPFL2005

HMM mixtures (HMM2) for robust speech recognition

Katrin Weber

State-of-the-art automatic speech recognition (ASR) techniques are typically based on hidden Markov models (HMMs) for the modeling of temporal sequences of feature vectors extracted from the speech signal. At the level of each HMM state, Gaussian mixture m ...

EPFL2003

HMM Mixtures (HMM2) for Robust Speech Recognition

Katrin Weber

State-of-the-art automatic speech recognition (ASR) techniques are typically based on hidden Markov models (HMMs) for the modeling of temporal sequences of feature vectors extracted from the speech signal. At the level of each HMM state, Gaussian mixture m ...

Ecole Polytechnique Federale de Lausanne2003

HMM Mixtures (HMM2) for Robust Speech Recognition

Katrin Weber

State-of-the-art automatic speech recognition (ASR) techniques are typically based on hidden Markov models (HMMs) for the modeling of temporal sequences of feature vectors extracted from the speech signal. At the level of each HMM state, Gaussian mixture m ...

IDIAP2003

HMM inference towards flexible speech recognition

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Acoustic Models for Posterior Features in Speech Recognition

A Bayesian Switching Linear Dynamical System for Scale-Invariant robust speech extraction

On Variable-Scale Piecewise Stationary Spectral Analysis of Speech Signals for ASR

Analysis and Classification of EEG Signals using Probabilistic Models for Brain Computer Interfaces

Analysis and classification of EEG signals using probabilistic models for brain computer interfaces

On Variable-Scale Piecewise Stationary Spectral Analysis of Speech Signals for ASR

Efficient integration of automated speech recognition in the framework of dialogue-based vocal systems

HMM mixtures (HMM2) for robust speech recognition

HMM Mixtures (HMM2) for Robust Speech Recognition

HMM Mixtures (HMM2) for Robust Speech Recognition

Acoustic Models for Posterior Features in Speech Recognition

HMM Mixtures (HMM2) for Robust Speech Recognition

Efficient integration of automated speech recognition in the framework of dialogue-based vocal systems

A Bayesian Switching Linear Dynamical System for Scale-Invariant robust speech extraction

On Variable-Scale Piecewise Stationary Spectral Analysis of Speech Signals for ASR

Analysis and Classification of EEG Signals using Probabilistic Models for Brain Computer Interfaces

HMM mixtures (HMM2) for robust speech recognition

Analysis and classification of EEG signals using probabilistic models for brain computer interfaces

On Variable-Scale Piecewise Stationary Spectral Analysis of Speech Signals for ASR

HMM Mixtures (HMM2) for Robust Speech Recognition