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Hilbert Envelope Based Features for Far-Field Speech Recognition

Publications associées (48)

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Filter Bank Design based on Minimization of Individual Aliasing Terms for Minimum Mutual Information Subband Adaptive Beamforming

Philip Neil Garner, Weifeng Li

This paper presents new filter bank design methods for sub- band adaptive beamforming. In this work, we design analysis and synthesis prototypes for modulated filter banks so as to minimize each aliasing term individually. We then drive the total response ...

2008

Adaptive Beamforming with a Maximum Negentropy Criterion

Philip Neil Garner, Weifeng Li

In this paper, we address an adaptive beamforming application in realistic acoustic conditions. After the position of a speaker is estimated by a speaker tracking system, we construct a subband-domain beamformer in generalized sidelobe canceller (GSC) conf ...

2008

Adaptive Beamforming with a Maximum Negentropy Criterion

Philip Neil Garner, Weifeng Li

\begin{abstract} In this paper, we address an adaptive beamforming application in realistic acoustic conditions. After the position of a speaker is estimated by a speaker tracking system, we construct a subband-domain beamformer in \emph{generalized sidelo ...

IDIAP2008

Using Pitch as Prior Knowledge in Template-Based Speech Recognition

Hervé Bourlard, Guillermo Aradilla

In a previous paper on speech recognition, we showed that templates can better capture the dynamics of speech signal compared to parametric models such as hidden Markov models. The key point in template matching approaches is finding the most similar templ ...

2006

Towards using slide information to enhance speech transcription of meetings

Hervé Bourlard, Artem Peregoudov, Alessandro Vinciarelli

In this paper we investigate the possibility of improving the speech recognition performance of meeting recordings by using slides captured during the recording process. The key hypothesis exploited in this work is that both slides and speech carry correla ...

IDIAP2006

Novel speech processing techniques for robust automatic speech recognition

Vivek Tyagi

The goal of this thesis is to develop and design new feature representations that can improve the automatic speech recognition (ASR) performance in clean as well noisy conditions. One of the main shortcomings of the fixed scale (typically 20-30 ms long ana ...

EPFL2006

In this paper, we investigate the possibility of enhancing state-of-the-art HMM-based speech recognition systems using data-driven techniques, where whole set of training utterances is used as reference models and recognition is then performed through the ...

IDIAP2005

Improving Speech Recognition Using a Data-Driven Approach

Hervé Bourlard, Guillermo Aradilla

2005

Using Pitch as Prior Knowledge in Template-Based Speech Recognition

Hervé Bourlard, Guillermo Aradilla

IDIAP2005

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