Automatic Spoken Language Identification Utilizing Acoustic and Phonetic Speech Information

179 Pages·2004·0.68 MB·English

Automatic Spoken Language
Identification Utilizing Acoustic
and Phonetic Speech Information
by
Kim-Yung Eddie Wong, BEng(Hons), BIT
PhD Thesis
Submitted in Fulfilment
of the Requirements
for the Degree of
Doctor of Philosophy
at the
Queensland University of Technology
Speech and Audio Research Laboratory
School of Electrical & Electronic Systems Engineering
June 2004

Substitute the “official” thesis
signature page here.

Keywords
Language identification, universal background model, vocal tract length normal-
ization, phone recognition, output score fusion, multi-lingual phone recognition,
cross-lingual speech recognition
vi
Abstract
Automatic spoken Language Identification (LID) is the process of identifying the
languagespokenwithinanutterance. Thechallengethatthistaskpresentsisthat
no prior information is available indicating the content of the utterance or the
identity of the speaker. The trend of globalization and the pervasive popularity of
the Internet will amplify the need for the capabilities spoken language identifica-
tion systems provide. A prominent application arises in call centers dealing with
speakers speaking different languages. Another important application is to index
or search huge speech data archives and corpora that contain multiple languages.
The aim of this research is to develop techniques targeted at producing a fast and
more accurate automatic spoken LID system compared to the previous National
Institute of Standards and Technology (NIST) Language Recognition Evaluation.
Acoustic and phonetic speech information are targeted as the most suitable fea-
tures for representing the characteristics of a language. To model the acoustic
speech features a Gaussian Mixture Model based approach is employed. Pho-
netic speech information is extracted using existing speech recognition technol-
ogy. Various techniques to improve LID accuracy are also studied. One approach
examined is the employment of Vocal Tract Length Normalization to reduce the
speech variation caused by different speakers. A linear data fusion technique is
adopted to combine the various aspects of information extracted from speech.
As a result of this research, a LID system was implemented and presented for
evaluation in the 2003 Language Recognition Evaluation conducted by the NIST.
Contents
Abstract i
List of Tables x
List of Figures xiii
Acronyms & Abbreviations xvi
Certification of Thesis xviii
Acknowledgments xix
Chapter 1 Introduction 1
1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Scope and Aim . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.3 Outline of Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.4 Major Contributions of the Research . . . . . . . . . . . . . . . . 6
CONTENTS iii
1.5 Publications Resulting from this Research . . . . . . . . . . . . . 7
Chapter 2 Background of Automatic Spoken Language Identifica-
tion 9
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.2 Automatic Language Identification in General . . . . . . . . . . . 11
2.3 Speech Information Utilized for Language Identification . . . . . . 12
2.3.1 Spoken Level Speech Information . . . . . . . . . . . . . . 13
2.3.2 Word Level Speech Information . . . . . . . . . . . . . . . 15
2.4 Automatic Language Identification Systems . . . . . . . . . . . . 16
2.4.1 Utilizing Acoustic Information . . . . . . . . . . . . . . . . 16
2.4.2 Utilizing Phonetic Information . . . . . . . . . . . . . . . . 18
2.4.3 Utilizing Prosodic Information . . . . . . . . . . . . . . . . 19
2.4.4 Utilizing Word Level Information . . . . . . . . . . . . . . 19
2.5 Difficulties of LID Research . . . . . . . . . . . . . . . . . . . . . 20
2.6 Resources for LID Research . . . . . . . . . . . . . . . . . . . . . 21
2.6.1 Oregon Graduate Institute Telephone Speech Corpus . . . 21
2.6.2 OGI 22 Language Telephone Speech Corpus . . . . . . . . 23
2.6.3 LDC CALLFRIEND Telephone Speech Database . . . . . 23
2.6.4 International Phonetic Alphabet . . . . . . . . . . . . . . . 24
iv CONTENTS
2.7 NIST Language Identification/Recognition Evaluation . . . . . . . 25
2.7.1 1993/1994 Language Identification Evaluation . . . . . . . 25
2.7.2 1996 Language Recognition Evaluation . . . . . . . . . . . 26
2.7.3 2003 Language Recognition Evaluation . . . . . . . . . . . 28
2.8 Summary and Discussion . . . . . . . . . . . . . . . . . . . . . . . 28
Chapter 3 Language Identification Utilizing Efficient Gaussian
Mixture Analysis 31
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
3.2 Gaussian Mixture Model Based LID System . . . . . . . . . . . . 33
3.2.1 Gaussian Mixture Model . . . . . . . . . . . . . . . . . . . 34
3.2.2 GMM based Language Identification . . . . . . . . . . . . 37
3.3 Universal Background Model Technique . . . . . . . . . . . . . . . 38
3.3.1 Background Speaker Modelling . . . . . . . . . . . . . . . 38
3.3.2 Universal Background Model . . . . . . . . . . . . . . . . . 39
3.3.3 Application of UBM Technique in LID . . . . . . . . . . . 42
3.4 Acoustic Information based LID Experiments . . . . . . . . . . . 45
3.4.1 System Setting . . . . . . . . . . . . . . . . . . . . . . . . 45
3.4.2 Experimental Results . . . . . . . . . . . . . . . . . . . . . 46