Publications related to Grapheme-based Automatic Speech Recognition using KL-HMM

Fast Approximate Spoken Term Detection from Sequence of Phonemes

We investigate the detection of spoken terms in conversational speech using phoneme recognition with the objective of achieving smaller index size as well as faster search speed. Speech is processed and indexed as a sequence of one best phoneme sequence. W ...

IDIAP2008

On Confusions in a Phoneme Recognizer

Hynek Hermansky, Joel Praveen Pinto

In this paper, we analyze the confusions patterns at three places in the hybrid phoneme recognition system. The confusions are analyzed at the pronunciation, the posterior probability, and the phoneme recognizer levels. The confusions show significant stru ...

2007

On Confusions in a Phoneme Recognizer

Hynek Hermansky, Joel Praveen Pinto

In this paper, we analyze the confusions patterns at three places in the hybrid phoneme recognition system. The confusions are analyzed at the pronunciation, the posterior probability, and the phoneme recognizer levels. The confusions show significant stru ...

IDIAP2007

Improving Continuous Speech Recognition System Performance with Grapheme Modelling

Hervé Bourlard, Hynek Hermansky, John David Scott Dines

This paper investigates automatic speech recognition system using context-dependent graphemes as subword units based on the conventional HMM/GMM system as well as TANDEM system. Experimental studies conducted on two different continuous speech recognition ...

IDIAP2005

Using auxiliary sources of knowledge for automatic speech recognition

Mathew Magimai Doss

Standard hidden Markov model (HMM) based automatic speech recognition (ASR) systems usually use cepstral features as acoustic observation and phonemes as subword units. Speech signal exhibits wide range of variability such as, due to environmental variatio ...

EPFL2005

Using Auxiliary Sources of Knowledge for Automatic Speech Recognition

Standard hidden Markov model (HMM) based automatic speech recognition (ASR) systems usually use cepstral features as acoustic observation and phonemes as subword units. Speech signal exhibits wide range of variability such as, due to environmental variatio ...

École Polytechnique Fédérale de Lausanne, Computer Science Department2005

Using Auxiliary Sources of Knowledge for Automatic Speech Recognition

Standard hidden Markov model (HMM) based automatic speech recognition (ASR) systems usually use cepstral features as acoustic observation and phonemes as subword units. Speech signal exhibits wide range of variability such as, due to environmental variatio ...

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

Joint Decoding for Phoneme-Grapheme Continuous Speech Recognition

Hervé Bourlard, Samy Bengio

Standard ASR systems typically use phoneme as the subword units. Preliminary studies have shown that the performance of the ASR system could be improved by using grapheme as additional subword units. In this paper, we investigate such a system where the wo ...

2004

Joint Decoding for Phoneme-Grapheme Continuous Speech Recognition

Hervé Bourlard, Samy Bengio

Standard ASR systems typically use phoneme as the subword units. Preliminary studies have shown that the performance of the ASR system could be improved by using grapheme as additional subword units. In this paper, we investigate such a system where the wo ...

IDIAP2003

Grapheme-based Automatic Speech Recognition using KL-HMM

Graph Chatbot

Chat with Graph Search

Fast Approximate Spoken Term Detection from Sequence of Phonemes

On Confusions in a Phoneme Recognizer

On Confusions in a Phoneme Recognizer

Improving Continuous Speech Recognition System Performance with Grapheme Modelling

Using auxiliary sources of knowledge for automatic speech recognition

Using Auxiliary Sources of Knowledge for Automatic Speech Recognition

Using Auxiliary Sources of Knowledge for Automatic Speech Recognition

Phoneme vs Grapheme Based Automatic Speech Recognition

Joint Decoding for Phoneme-Grapheme Continuous Speech Recognition

Joint Decoding for Phoneme-Grapheme Continuous Speech Recognition

Improving Continuous Speech Recognition System Performance with Grapheme Modelling

Joint Decoding for Phoneme-Grapheme Continuous Speech Recognition

Fast Approximate Spoken Term Detection from Sequence of Phonemes

On Confusions in a Phoneme Recognizer

Using Auxiliary Sources of Knowledge for Automatic Speech Recognition

Using Auxiliary Sources of Knowledge for Automatic Speech Recognition

On Confusions in a Phoneme Recognizer

Using auxiliary sources of knowledge for automatic speech recognition

Phoneme vs Grapheme Based Automatic Speech Recognition

Joint Decoding for Phoneme-Grapheme Continuous Speech Recognition