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Spectro-Temporal Activity Pattern (STAP) Features for Noise Robust ASR

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Noise-Robust Multi-Stream Fusion for Text-Independent Speaker Authentication

Samy Bengio, Norman Hoon Thian Poh

Multi-stream approaches have proven to be very successful in speech recognition tasks and to a certain extent in speaker authentication tasks. In this study we propose a noise-robust multi-stream text-independent speaker authentication system. This system ...

IDIAP2004

Noise-Robust Multi-Stream Fusion for Text-Independent Speaker Authentication

Samy Bengio, Norman Hoon Thian Poh

2004

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

Phase AutoCorrelation (PAC) derived Robust Speech Features

Hervé Bourlard, Hemant Misra, Shajith Ikbal

In this paper, we introduce a new class of noise robust acoustic features derived from a new measure of autocorrelation, and explicitly exploiting the phase variation of the speech signal frame over time. This family of features, referred to as ``Phase Aut ...

2003

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 ...

2003

On Multi-scale Fourier Transform Analysis of Speech Signals

Hervé Bourlard, Vivek Tyagi

In this paper, we introduce a novel algorithm to perform multi-scale Fourier transform analysis of piecewise stationary signals with application to automatic speech recognition. Such signals are composed of quasi-stationary segments of variable lengths. Th ...

IDIAP2003

Noise PDF transformation in secondary feature processing

Motivated by the human ability to maintain a high level of speech recognition when large parts of the spectrogram are masked (i.e. dominated) by noise, the original "missing data" (MD) approach to noise robust speech recognition was based on the paradigm w ...

IDIAP2002

Robust speech recognition based on multi-stream processing

Astrid Hagen

Despite sophisticated present day automatic speech recognition (ASR) techniques, a single recognizer is usually incapable of accounting for the varying conditions in a typical natural environment. Higher robustness to a range of noise cases can potentially ...

EPFL2002