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Sparse and Low-rank Modeling for Automatic Speech Recognition

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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

Ecole Polytechnique Federale de Lausanne2003

HMM Mixtures (HMM2) for Robust Speech Recognition

Katrin Weber

IDIAP2003

Offline Cursive Handwriting: From Word To Text Recognition

Alessandro Vinciarelli

This thesis presents a PhD work on offline cursive handwriting recognition, the automatic transcription of cursive data when only its image is available. Two main approaches were used in the literature to solve the problem. The first one attempts to segmen ...

IDIAP2003

Speech/Music Discrimination using Entropy and Dynamism Features in a HMM Classification Framework

Hervé Bourlard, Jitendra Ajmera

In this paper, we present a new approach towards high performance speech/music discrimination on realistic tasks related to the automatic transcription of broadcast news. In the approach presented here, the (local) Probability Density Function (PDF) estima ...

2003

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

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 ...

IDIAP2002

Speaker Normalization using HMM2

Hervé Bourlard, Katrin Weber, Shajith Ikbal

2002

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