Publications related to Exploiting Contextual Information for Improved Phoneme Recognition

Audio-Visual Speech Recognition with a Hybrid SVM-HMM System

Traditional speech recognition systems use Gaussian mixture models to obtain the likelihoods of individual phonemes, which are then used as state emission probabilities in hidden Markov models representing the words. In hybrid systems, the Gaussian mixture ...

EUSIPCO2005

Modeling Individual and Group Actions in Meetings: a Two-Layer HMM Framework

Daniel Gatica-Perez, Samy Bengio, Guillaume Lathoud, Dong Zhang

We address the problem of recognizing sequences of human interaction patterns in meetings, with the goal of structuring them in semantic terms. The investigated patterns, are inherently group-based (defined by the individual activities of meeting participa ...

2004

Modeling Individual and Group Actions in Meetings: a Two-Layer HMM Framework

Daniel Gatica-Perez, Samy Bengio, Guillaume Lathoud, Dong Zhang

We address the problem of recognizing sequences of human interaction patterns in meetings, with the goal of structuring them in semantic terms. The investigated patterns, are inherently group-based (defined by the individual activities of meeting participa ...

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

Speech recognition with auxiliary information

Automatic speech recognition (ASR) is a very challenging problem due to the wide variety of the data that it must be able to deal with. Being the standard tool for ASR, hidden Markov models (HMMs) have proven to work well for ASR when there are controls ov ...

EPFL2003

Speech Recognition with Auxiliary Information

Automatic speech recognition (ASR) is a very challenging problem due to the wide variety of the data that it must be able to deal with. Being the standard tool for ASR, hidden Markov models (HMMs) have proven to work well for ASR when there are controls ov ...

IDIAP2003

Speech Recognition with Auxiliary Information

Automatic speech recognition (ASR) is a very challenging problem due to the wide variety of the data that it must be able to deal with. Being the standard tool for ASR, hidden Markov models (HMMs) have proven to work well for ASR when there are controls ov ...

École Polytechnique Fédérale de Lausanne, Computer Science Department2003

Automatic Speech Recognition using Dynamic Bayesian Networks with the Energy as an Auxiliary Variable

In current automatic speech recognition (ASR) systems, the energy is not used as part of the feature vector in spite of being a fundamental feature in the speech signal. The noise inherent in its estimation degrades the system performance. In this report w ...

IDIAP2003

Exploiting Contextual Information for Improved Phoneme Recognition

Graph Chatbot

Chat with Graph Search

Audio-Visual Speech Recognition with a Hybrid SVM-HMM System

Modeling Individual and Group Actions in Meetings: a Two-Layer HMM Framework

Modeling Individual and Group Actions in Meetings: a Two-Layer HMM Framework

HMM mixtures (HMM2) for robust speech recognition

HMM Mixtures (HMM2) for Robust Speech Recognition

HMM Mixtures (HMM2) for Robust Speech Recognition

Speech recognition with auxiliary information

Speech Recognition with Auxiliary Information

Speech Recognition with Auxiliary Information

Automatic Speech Recognition using Dynamic Bayesian Networks with the Energy as an Auxiliary Variable

Audio-Visual Speech Recognition with a Hybrid SVM-HMM System

HMM mixtures (HMM2) for robust speech recognition

HMM Mixtures (HMM2) for Robust Speech Recognition

HMM Mixtures (HMM2) for Robust Speech Recognition

Speech recognition with auxiliary information

Speech Recognition with Auxiliary Information

Speech Recognition with Auxiliary Information

Automatic Speech Recognition using Dynamic Bayesian Networks with the Energy as an Auxiliary Variable

Modeling Individual and Group Actions in Meetings: a Two-Layer HMM Framework

Modeling Individual and Group Actions in Meetings: a Two-Layer HMM Framework