Publications related to Increasing Speech Recognition Noise Robustness with HMM2

On Variable-Scale Piecewise Stationary Spectral Analysis of Speech Signals for ASR

It is often acknowledged that speech signals contain short-term and long-term temporal properties that are difficult to capture and model by using the usual fixed scale (typically 20ms) short time spectral analysis used in hidden Markov models (HMMs), base ...

IDIAP2005

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 ...

IDIAP2005

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

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 ...

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

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 ...

Ecole Polytechnique Federale de Lausanne2003

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 ...

IDIAP2003

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

Increasing Speech Recognition Noise Robustness with HMM2

Hervé Bourlard, Samy Bengio, Katrin Weber

The purpose of this paper is to investigate the behavior of HMM2 models for the recognition of noisy speech. It has previously been shown that HMM2 is able to model dynamically important structural information inherent in the speech signal, often correspon ...

2002

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 ...

IDIAP2001

Increasing Speech Recognition Noise Robustness with HMM2

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On Variable-Scale Piecewise Stationary Spectral Analysis of Speech Signals for ASR

Using Pitch as Prior Knowledge in Template-Based Speech Recognition

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

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

HMM mixtures (HMM2) for robust speech recognition

HMM Mixtures (HMM2) for Robust Speech Recognition

HMM Mixtures (HMM2) for Robust Speech Recognition

Robust Speech Recognition and Feature Extraction Using HMM2

Increasing Speech Recognition Noise Robustness with HMM2

Robust Speech Recognition and Feature Extraction Using HMM2

Using Pitch as Prior Knowledge in Template-Based Speech Recognition

On Variable-Scale Piecewise Stationary Spectral Analysis of Speech Signals for ASR

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

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

HMM mixtures (HMM2) for robust speech recognition

Robust Speech Recognition and Feature Extraction Using HMM2

HMM Mixtures (HMM2) for Robust Speech Recognition

HMM Mixtures (HMM2) for Robust Speech Recognition

Increasing Speech Recognition Noise Robustness with HMM2

Robust Speech Recognition and Feature Extraction Using HMM2