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SELECTING RELEVANT VISUAL FEATURES FOR SPEECHREADING

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Relevant Feature Selection for Audio-Visual Speech Recognition

Jean-Philippe Thiran, Mihai Gurban, Thomas Drugman

We present a feature selection method based on information theoretic measures, targeted at multimodal signal processing, showing how we can quantitatively assess the relevance of features from different modalities. We are able to find the features with the ...

2007

Low-Dimensional Motion Features for Audio-Visual Speech Recognition

Jean-Philippe Thiran, Mihai Gurban, Andrés Vallés

Audio-visual speech recognition promises to improve the performance of speech recognizers, especially when the audio is corrupted, by adding information from the visual modality, more specifically, from the video of the speaker. However, the number of visu ...

2007

Variational Information Maximization in Gaussian Channels

Recently, we introduced a simple variational bound on mutual information, that resolves some of the difficulties in the application of information theory to machine learning. Here we study a specific application to Gaussian channels. It is well known that ...

IDIAP2004

Kernel-based classifiers with applications to face detection

Vlad Popovici

Statistical pattern recognition occupies a central place in the general context of machine learning techniques, as it provides the theoretical insights and the practical means for solving a variety of problems ranging from character recognition to face rec ...

EPFL2004

Evaluation of Formant-Like Features for ASR

Hervé Bourlard, Samy Bengio, Katrin Weber

This paper investigates possibilities to automatically find a low-dimensional, formant-related physical representation of the speech signal, which is suitable for automatic speech recognition (ASR). This aim is motivated by the fact that formants have been ...

IDIAP2002

Evaluation of Formant-Like Features for ASR

Hervé Bourlard, Samy Bengio, Katrin Weber

2002

PCA in Autocorrelation Space

Jean-Philippe Thiran, Vlad Popovici

The use of higher order autocorrelations as features for pattern classification has been usually restricted to second or third orders due to high computational costs. Since the autocorrelation space is a high dimensional space we are interested in reducing ...

SPIE2002

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

Mixture models for unsupervised and supervised learning

In a society which produces and consumes an ever increasing amount of information, methods which can make sense out of al1 this data become of crucial importance. Machine learning tries to develop models which can make the information load accessible. Thre ...

EPFL2000