Acoustic feature combination for speech recognition
type: CreativeWork
description: In this thesis, the use of multiple acoustic features of the speech signal is considered for speech recognition. The goals of this thesis are twofold: on the one hand, new acoustic features are developed, on the other hand, feature combination methods are investigated in order to find an effective integration of the newly developed features into state-of-the-art speech recognition systems. The most commonly used feature extraction methods are the Mel Frequency Cepstrum Coefficients (MFCC), Perceptual Linear Prediction (PLP), and variations of these techniques. These methods are mainly based on the models of the human auditory system. A detailed review of the implementation of these features is presented in this thesis. There have also been attempts at using articulatory motivated acoustic features for speech recognition which are motivated by models of the human speech production system. This thesis focuses partially on the development of new articulatory motivated acoustic features. The voicing information is one of the most commonly used articulatory features. Three voicing extraction methods are presented in this work followed by a systematic comparison. Besides the analysis of the voicing feature, the novel spectrum derivative feature is introduced which aims to capture the differences between magnitude spectra produced by obstruent and sonant consonants. The articulatory motivated features are tested in combinations with state-of-the-art acoustic features based on auditory models mainly. The features are combined both directly using Linear Discriminant Analysis (LDA) as well as indirectly on model level using Discriminative Model Combination (DMC). Both methods have already been used successfully in automatic speech recognition systems. In this work, a comparative study is presented which describes and analyzes the application of these methods to feature combination. Robustness issues of the LDA based method are addressed which are induced by increasing the amo…
abstract: In this thesis, the use of multiple acoustic features of the speech signal is considered for speech recognition. The goals of this thesis are twofold: on the one hand, new acoustic features are developed, on the other hand, feature combination methods are investigated in order to find an effective integration of the newly developed features into state-of-the-art speech recognition systems. The most commonly used feature extraction methods are the Mel Frequency Cepstrum Coefficients (MFCC), Perceptual Linear Prediction (PLP), and variations of these techniques. These methods are mainly based on the models of the human auditory system. A detailed review of the implementation of these features is presented in this thesis. There have also been attempts at using articulatory motivated acoustic features for speech recognition which are motivated by models of the human speech production system. This thesis focuses partially on the development of new articulatory motivated acoustic features. The voicing information is one of the most commonly used articulatory features. Three voicing extraction methods are presented in this work followed by a systematic comparison. Besides the analysis of the voicing feature, the novel spectrum derivative feature is introduced which aims to capture the differences between magnitude spectra produced by obstruent and sonant consonants. The articulatory motivated features are tested in combinations with state-of-the-art acoustic features based on auditory models mainly. The features are combined both directly using Linear Discriminant Analysis (LDA) as well as indirectly on model level using Discriminative Model Combination (DMC). Both methods have already been used successfully in automatic speech recognition systems. In this work, a comparative study is presented which describes and analyzes the application of these methods to feature combination. Robustness issues of the LDA based method are addressed which are induced by increasing the amo…
author: Zolnay, András
language: eng
keywords: info:eu-repo/classification/ddc/004, Informatik, Automatische Spracherkennung, Merkmalsextraktion, Merkmal, Kombination, Articulatory Features, Voicedness Feature, Spectrum Derivative Feature, Linear Discriminant Analysis, Discriminative Model Combination
date published: 2006
date modified: 2026-07-30T19:43:10Z
provider: RWTH Aachen University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Publikationsserver der RWTH Aachen University