Publication

Acoustic models for posterior features in speech recognition

Related publications (135)

Graph Chatbot

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

Towards using hierarchical posteriors for flexible automatic speech recognition systems

Hervé Bourlard, Samy Bengio, Bertrand Mesot

Local state (or phone) posterior probabilities are often investigated as local classifiers (e.g., hybrid HMM/ANN systems) or as transformed acoustic features (e.g., ``Tandem'') towards improved speech recognition systems. In this paper, we present initial ...

IDIAP2004

Robust Audio Segmentation

Hervé Bourlard, Jitendra Ajmera

Audio segmentation, in general, is the task of segmenting a continuous audio stream in terms of acoustically homogenous regions, where the rule of homogeneity depends on the task. This thesis aims at developing and investigating efficient, robust and unsup ...

IDIAP2004

Robust Audio Segmentation

Hervé Bourlard, Jitendra Ajmera

École Polytechnique Fédérale de Lausanne2004

Phase AutoCorrelation (PAC) features in Entropy based Multi-Stream for Robust Speech Recognition

Hervé Bourlard, Hynek Hermansky, Hemant Misra, Shajith Ikbal

Methods to improve noise robustness of speech recognition systems often result in degradation of recognition performance for clean speech. Recently proposed Phase AutoCorrelation (PAC) \cite{ikbal03,ikbal03a} based features, showing noticeable improvement ...

2004

Modelling Auxiliary Features in Tandem Systems

Hervé Bourlard, Shajith Ikbal

Tandem systems transform the cepstral features into posterior probabilities of subword units using artificial neural networks (ANNs), which are processed to form input features for conventional speech recognition systems. They have been shown to perform be ...

2004

Modelling Auxiliary Features in Tandem Systems

Hervé Bourlard, Shajith Ikbal

IDIAP2004

Validation of Tissue Modelization and Classification Techniques in T1-weighted MR Brain Images

Jean-Philippe Thiran, Meritxell Bach Cuadra, Leila Cammoun, Olivier Cuisenaire, Torsten Butz

We propose a deep study on tissue modelization and classification techniques on T1-weighted MR images. Six approaches have been taken into account to perform this validation study. We consider first the Finite Gaussian Mixture model (A-FGMM) and a Bayes cl ...

2004

An Auxiliary Variational Method

Variational methods have proved popular and effective for inference and learning in intractable graphical models. An attractive feature of the approaches based on the Kullback-Leibler divergence is a rigorous lower bound on the normalization constants in u ...

IDIAP2004

On Use of Task Independent Training Data in Tandem Feature Extraction

Hynek Hermansky

The problem we address in this paper is, whether the feature extraction module trained on large amounts of task independent data, can improve the performance of stochastic models? We show that when there is only a small amount of task specific training dat ...

2004

On the Combination of Speech and Speaker Recognition

Hervé Bourlard

This paper investigates an approach that maximizes the joint posterior probabil ity of the pronounced word and the speaker identity given the observed data. This probability can be expressed as a product of the posterior probability of the pronounced word ...

IDIAP2003