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Samuel Thomas

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Publications associées (19)

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Subspace Gaussian Mixture Models for speech recognition

Pinar Akyazi, Samuel Thomas

We describe an acoustic modeling approach in which all phonetic states share a common Gaussian Mixture Model structure, and the means and mixture weights vary in a subspace of the total parameter space. We call this a Subspace Gaussian Mixture Model (SGMM) ...

2010

Multilingual acoustic modeling for speech recognition based on subspace Gaussian Mixture Models

Pinar Akyazi, Samuel Thomas

Although research has previously been done on multilingual speech recognition, it has been found to be very difficult to improve over separately trained systems. The usual approach has been to use some kind of “universal phone set” that covers multiple lan ...

IEEE2010

Approaches to automatic lexicon learning with limited training examples

Pinar Akyazi, Samuel Thomas

Preparation of a lexicon for speech recognition systems can be a significant effort in languages where the written form is not exactly phonetic. On the other hand, in languages where the written form is quite phonetic, some common words are often mispronou ...

IEEE2010

A novel estimation of feature-space MLLR for full-covariance models

Pinar Akyazi, Samuel Thomas

In this paper we present a novel approach for estimating feature-space maximum likelihood linear regression (fMLLR) transforms for full-covariance Gaussian models by directly maximizing the likelihood function by repeated line search in the direction of th ...

IEEE2010

Phoneme Recognition Using Spectral Envelope and Modulation Frequency Features

Hynek Hermansky, Sriram Ganapathy, Samuel Thomas

We present a new feature extraction technique for phoneme recognition that uses short-term spectral envelope and modulation frequency features. These features are derived from sub-band temporal envelopes of speech estimated using Frequency Domain Linear Pr ...

Idiap2009

APPLICATIONS OF SIGNAL ANALYSIS USING AUTOREGRESSIVE MODELS FOR AMPLITUDE MODULATION

Petr Motlicek, Hynek Hermansky, Sriram Ganapathy, Samuel Thomas

Frequency Domain Linear Prediction (FDLP) represents an efficient technique for representing the long-term amplitude modulations (AM) of speech/audio signals using autoregressive models. For the proposed analysis technique, relatively long temporal segment ...

Idiap2009

APPLICATIONS OF SIGNAL ANALYSIS USING AUTOREGRESSIVE MODELS FOR AMPLITUDE MODULATION

Petr Motlicek, Hynek Hermansky, Sriram Ganapathy, Samuel Thomas

2009

Recognition Of Reverberant Speech Using Frequency Domain Linear Prediction

Hynek Hermansky, Sriram Ganapathy, Samuel Thomas

Performance of a typical automatic speech recognition (ASR) system severely degrades when it encounters speech from reverberant environments. Part of the reason for this degradation is the feature extraction techniques that use analysis windows which are m ...

2008

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IDIAP2008

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In this letter, a new feature extraction technique based on modulation spectrum derived from syllable-length segments of sub-band temporal envelopes is proposed. These sub-band envelopes are derived from auto-regressive modelling of Hilbert envelopes of th ...

2008