Word posterior probabilities for large vocabulary continuous speech recognition
type: CreativeWork
description: In this thesis, the use of word posterior probabilities for large vocabulary continuous speech recognition is investigated in a unified, statistical framework. The word posterior probabilities are directly derived from the sentence posterior probabilities which are an essential part of Bayes' Decision Rule. Different approaches to the computation of these probabilities using N-best lists and word graphs are discussed, both theoretically and experimentally. The word posterior probabilities are used as confidence measures for various applications. It is shown that these probabilities are the best confidence measure among those studied in this work. The performance of the confidence measures is evaluated in a unified framework using two evaluation metrics and five highly different speech corpora. The relative reduction of the confidence error rates with the word posterior probabilities ranges between 18.6% and 35.4%. In order to show the usefulness of the suggested confidence measure, the word posterior probabilities are applied to restrict maximum-likelihood-linear-regression adaptation to those acoustic segments with a high confidence. In doing so, incorrectly recognised parts of the transcription can be excluded from the adaptation algorithm. Using this method, the word error rate is reduced by 4.8% relative on a German spontaneous-speech test set. In a very similar manner, the word posterior probabilities are used to train an American Broadcast News recogniser with automatically generated, i.e., recognised transcriptions. Only those parts of the acoustic training corpus are used where the confidence of the transcription is sufficiently high. In order to bootstrap an initial low-cost speech recognition system which can be used to recognise large quantities of untranscribed speech data for training purposes, a small amount of speech is transcribed manually. This small speech database with the manually generated transcriptions is then used to train the low-cost speech…
abstract: In this thesis, the use of word posterior probabilities for large vocabulary continuous speech recognition is investigated in a unified, statistical framework. The word posterior probabilities are directly derived from the sentence posterior probabilities which are an essential part of Bayes' Decision Rule. Different approaches to the computation of these probabilities using N-best lists and word graphs are discussed, both theoretically and experimentally. The word posterior probabilities are used as confidence measures for various applications. It is shown that these probabilities are the best confidence measure among those studied in this work. The performance of the confidence measures is evaluated in a unified framework using two evaluation metrics and five highly different speech corpora. The relative reduction of the confidence error rates with the word posterior probabilities ranges between 18.6% and 35.4%. In order to show the usefulness of the suggested confidence measure, the word posterior probabilities are applied to restrict maximum-likelihood-linear-regression adaptation to those acoustic segments with a high confidence. In doing so, incorrectly recognised parts of the transcription can be excluded from the adaptation algorithm. Using this method, the word error rate is reduced by 4.8% relative on a German spontaneous-speech test set. In a very similar manner, the word posterior probabilities are used to train an American Broadcast News recogniser with automatically generated, i.e., recognised transcriptions. Only those parts of the acoustic training corpus are used where the confidence of the transcription is sufficiently high. In order to bootstrap an initial low-cost speech recognition system which can be used to recognise large quantities of untranscribed speech data for training purposes, a small amount of speech is transcribed manually. This small speech database with the manually generated transcriptions is then used to train the low-cost speech…
author: Wessel, Frank
language: eng
keywords: info:eu-repo/classification/ddc/004, Informatik
date published: 2002
date modified: 2026-07-30T19:43:19Z
provider: RWTH Aachen University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Publikationsserver der RWTH Aachen University