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

Using Pitch as Prior Knowledge in Template-Based Speech Recognition

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

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

Efficient integration of automated speech recognition in the framework of dialogue-based vocal systems

In this work, we propose different strategies for efficiently integrating an automated speech recognition module in the framework of a dialogue-based vocal system. The aim is the study of different ways leading to the improvement of the quality and robustn ...

EPFL2005

Nonlinear feature transformations for noise robust speech recognition

Shajith Ikbal

Robustness against external noise is an important requirement for automatic speech recognition (ASR) systems, when it comes to deploying them for practical applications. This thesis proposes and evaluates new feature-based approaches for improving the ASR ...

EPFL2004

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

LP-TRAP: Linear predictive temporal patterns

Hynek Hermansky

Autoregressive modeling is applied for approximating the temporal evolution of spectral density in critical-band-sized sub-bands of a segment of speech signal. The generalized autocorrelation linear predictive technique allows for a compromise between fitt ...

IDIAP2004

LP-TRAP: Linear predictive temporal patterns

Hynek Hermansky

Autoregressive modeling is applied for approximating the temporal evolution of spectral density in critical-band-sized sub-bands of a segment of speech signal. The generalized autocorrelation linear predictive technique allows for a compromise between fitt ...

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

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

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

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Using Pitch as Prior Knowledge in Template-Based Speech Recognition

Using Pitch as Prior Knowledge in Template-Based Speech Recognition

Efficient integration of automated speech recognition in the framework of dialogue-based vocal systems

Nonlinear feature transformations for noise robust speech recognition

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

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

LP-TRAP: Linear predictive temporal patterns

LP-TRAP: Linear predictive temporal patterns

HMM mixtures (HMM2) for robust speech recognition

HMM Mixtures (HMM2) for Robust Speech Recognition

Using Pitch as Prior Knowledge in Template-Based Speech Recognition

Using Pitch as Prior Knowledge in Template-Based Speech Recognition

HMM Mixtures (HMM2) for Robust Speech Recognition

LP-TRAP: Linear predictive temporal patterns

Nonlinear feature transformations for noise robust speech recognition

Efficient integration of automated speech recognition in the framework of dialogue-based vocal systems

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

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

LP-TRAP: Linear predictive temporal patterns

HMM mixtures (HMM2) for robust speech recognition