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On Use of Task Independent Training Data in Tandem Feature Extraction

Related publications (32)

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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

IDIAP2003

Speech Recognition with Auxiliary Information

École Polytechnique Fédérale de Lausanne, Computer Science Department2003

Multi-Modal Audio-Visual Event Recognition for Football Analysis

Jean-Marc Odobez, Samy Bengio, Mark Barnard

The recognition of events within multi-modal data is a challenging problem. In this paper we focus on the recognition of events by using both audio and video data. We investigate the use of data fusion techniques in order to recognise these sequences withi ...

2003

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

EPFL lab session 2/2: Introduction to Hidden Markov Models

Sacha Krstulovic

Lab sessions given in relation to Herve Bourlard's Speech Recognition course at EPFL (Ecole Polytechnique Federale de Lausanne), second semester 2001. The full session is available from the web as ftp://ftp.idiap.ch/pub/sacha/labs/Session2.tgz . ...

IDIAP2001

Speech Recognition Using Advanced HMM2 Features

Hervé Bourlard, Samy Bengio, Katrin Weber

HMM2 is a particular hidden Markov model where state emission probabilities of the temporal (primary) HMM are modeled through (secondary) state-dependent frequency-based HMMs [12]. As shown in [13], a secondary HMM can also be used to extract robust ASR fe ...

IDIAP2001

Speech Recognition Using Advanced HMM2 Features

Hervé Bourlard, Samy Bengio, Katrin Weber

2001

IDIAP HMM/HMM2 System: Theoretical Basis and Software Specifications

Hervé Bourlard, Samy Bengio, Katrin Weber, Shajith Ikbal

State-of-the-art Automatic Speech Recognition (ASR) systems make extensive use of Hidden Markov Models (HMMs), characterized by flexible statistical modeling, powerful optimization (training) techniques and efficient recognition algorithms. When allowed by ...

IDIAP2001

Combining Wavelet-domain Hidden Markov Trees with Hidden Markov Models

In this paper, the concept of Wavelet-domain Hidden Markov Trees (WHMT) is introduced to Automatic Speech Recognition. WHMT are a convenient means to model the structure of wavelet feature vectors, as wavelet coefficients can be interpreted as nodes in a b ...

IDIAP1999