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Robust speech recognition based on multi-stream processing

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A Frequency-Domain Silence Noise Model

Guillaume Lathoud, Bertrand Mesot

This paper proposes a simple, computationally efficient 2-mixture model approach to discrimination between speech and background noise. It is directly derived from observations on real data, and can be used in a fully unsupervised manner, with the EM algor ...

IDIAP2005

Acoustic Echo Cancellation for Human-Robot Communications

Jérôme Berclaz

This master thesis presents a new efficient method of acoustic echo cancellation targeted at speech recognition for robots. The proposed algorithm features a new double-talk detector, an enhanced initialization and a new noise estimation method. The DTD al ...

2004

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

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

Hervé Bourlard, Hemant Misra, Shajith Ikbal

In this paper, we introduce a new noise robust representation of speech signal obtained by locating points of potential importance in the spectrogram, and parameterizing the activity of time-frequency pattern around those points. These features are referre ...

2004

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

Hervé Bourlard, Hemant Misra, Shajith Ikbal

IDIAP2004

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

Stochastic resonance in pattern recognition by a holographic neuron model

Jonas Buchli

The recognition rate of holographic neural synapses, performing a pattern recognition task, is significantly higher when applied to natural, rather than artificial, images. This shortcoming of artificial images can be largely compensated for, if noise is a ...

2003

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