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Towards Weakly Supervised Acoustic Subword Unit Discovery and Lexicon Development Using Hidden Markov Models

Publications associées (46)

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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 ...

2006

A Toolbox for Easily Calibrating Omnidirectional Cameras

Roland Siegwart, Davide Scaramuzza

In this paper, we present a novel technique for calibrating central omnidirectional cameras. The proposed procedure is very fast and completely automatic, as the user is only asked to collect a few images of a checker board, and click on its corner points. ...

2006

Using Pitch as Prior Knowledge in Template-Based Speech Recognition

Hervé Bourlard, Guillermo Aradilla

IDIAP2005

Local Features and 1D-HMMs for Fast and Robust Face Authentication

It has been previously demonstrated that systems based on Hidden Markov Models (HMMs) are suitable for face recognition. The proposed approaches in the literature are either HMMs with one-dimensional (1D-HMMs) or two-dimensional (2D-HMMs) topology. Both ha ...

IDIAP2005

Phoneme vs Grapheme Based Automatic Speech Recognition

Hervé Bourlard, Hynek Hermansky, John David Scott Dines

In recent literature, different approaches have been proposed to use graphemes as subword units with implicit source of phoneme information for automatic speech recognition. The major advantage of using graphemes as subword units is that the definition of ...

IDIAP2004

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

École Polytechnique Fédérale de Lausanne, Computer Science Department2003

Gaussian mixture models for on-line signature verification

Jonas Richiardi, Andrzej Drygajlo

This paper introduces and motivates the use of Gaussian Mixture Models (GMMs) for on-line signature verification. The individual Gaussian components are shown to represent some local, signer-dependent features that characterise spatial and temporal aspects ...

2003

HMM inference towards flexible speech recognition

One of the difficulties in Automatic Speech Recognizer (ASR) is the pronunciation variability. Each word (modeled by a baseline phonetic transcription in the ASR dictionary) can be pronounced in many different ways depending on many complex qualitative and ...

IDIAP2003

Pronunciation models and their evaluation using confidence measures

Hervé Bourlard

In this report, we present preliminary experiments towards automatic inference and evaluation of pronunciation models based on multiple utterances of each lexicon word and their given baseline pronunciation model (baseform phonetic transcription). In the p ...

IDIAP2001