Modeling spontaneous speech variability for large vocabulary continuous speech recognition
type: CreativeWork
description: In this work a number of novel techniques for improved treatment of spontaneous speech variabilities in large vocabulary automatic speech recognition are developed and evaluated on US English conversational speech and spontaneous medical dictations. Two main aspects of spontaneous speech modeling are addressed: The general handling of pronunciation variability and the individual and parallel treatment of multiple speech variabilities in the acoustic and pronunciation model of a one-pass speech recognizer.The problem of an optimal incorporation of multiple alternative pronunciations into the search framework is addressed in the first part of the thesis. This includes the question of how to efficiently combine the probabilistic contributions of alternative pronunciations in the course of a left to right search procedure. The well known maximum approximation, usually applied in this context, is compared to a novel time synchronous sum approximation technique which integrates alternative pronunciations in a weighted sum of acoustic probabilities. It is shown on a conversational speech task that this approach outperforms the maximum approximation by 2% relative and reduces the search costs by 7%.Another important issue with respect to the incorporation of alternative pronunciations into the search framework is the statistical weighting of the pronunciations. The usually applied pronunciation unigram prior probabilities are typically estimated by the relative frequencies of pronunciations in the training hypotheses. This standard maximum likelihood solution is compared to a novel discriminative training scheme which is an extension of the Discriminative Model Combination technique, proposed in [Beyerlein 01]. The developed iterative reestimation procedure is shown to adjust the influence of a specific pronunciation prior probability in the discriminant function in dependence of (1) the word error rate, (2) the frequency of occurrence of this pronunciation in the correct h…
abstract: In this work a number of novel techniques for improved treatment of spontaneous speech variabilities in large vocabulary automatic speech recognition are developed and evaluated on US English conversational speech and spontaneous medical dictations. Two main aspects of spontaneous speech modeling are addressed: The general handling of pronunciation variability and the individual and parallel treatment of multiple speech variabilities in the acoustic and pronunciation model of a one-pass speech recognizer.The problem of an optimal incorporation of multiple alternative pronunciations into the search framework is addressed in the first part of the thesis. This includes the question of how to efficiently combine the probabilistic contributions of alternative pronunciations in the course of a left to right search procedure. The well known maximum approximation, usually applied in this context, is compared to a novel time synchronous sum approximation technique which integrates alternative pronunciations in a weighted sum of acoustic probabilities. It is shown on a conversational speech task that this approach outperforms the maximum approximation by 2% relative and reduces the search costs by 7%.Another important issue with respect to the incorporation of alternative pronunciations into the search framework is the statistical weighting of the pronunciations. The usually applied pronunciation unigram prior probabilities are typically estimated by the relative frequencies of pronunciations in the training hypotheses. This standard maximum likelihood solution is compared to a novel discriminative training scheme which is an extension of the Discriminative Model Combination technique, proposed in [Beyerlein 01]. The developed iterative reestimation procedure is shown to adjust the influence of a specific pronunciation prior probability in the discriminant function in dependence of (1) the word error rate, (2) the frequency of occurrence of this pronunciation in the correct h…
author: Schramm, Hauke
language: eng
keywords: info:eu-repo/classification/ddc/004, Informatik, Automatische Spracherkennung, Sprechtempo, Variabilität, Spontansprache, Sprachvariabilität, Lexikon, Aussprachevariation, Diskriminatives Training, spontaneous speech, speech variability, pronunciation lexicon, alternative pronunciation, discriminative training
date published: 2006
date modified: 2026-07-30T19:43:02Z
provider: RWTH Aachen University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Publikationsserver der RWTH Aachen University