Predictability of the effects of phoneme merging on speech recognition performance by quantifying phoneme relations

To investigate whether the impact of phoneme merging on recognition rate can be predicted, different measures to quantify the relationship between two phonemes a and b were compared: (1) the functional load of their opposition, (2) the bigram type preservation, (3) their information radius, (4) their distance within an information gain tree induced from a distinctive feature matrix, and (5) the symmetric Kullback-Leibler divergence. For each of 25 phoneme pairs we trained a speech recognizer on data in which the respective pair was merged. Based on correlation analyses and predictor selection in stepwise regression modelling we found that the impact of phoneme merging on accuracy can tentatively be captured in terms of functional load and tree distance between the merged phonemes

Pronunciation modeling for Cantonese speech recognition.

Kam Patgi.Thesis (M.Phil.)--Chinese University of Hong Kong, 2003.Includes bibliographical references (leaf 103).Abstracts in English and Chinese.Chapter Chapter 1. --- Introduction --- p.1Chapter 1.1 --- Automatic Speech Recognition --- p.1Chapter 1.2 --- Pronunciation Modeling in ASR --- p.2Chapter 1.3 --- Obj ectives of the Thesis --- p.5Chapter 1.4 --- Thesis Outline --- p.5Reference --- p.7Chapter Chapter 2. --- The Cantonese Dialect --- p.9Chapter 2.1 --- Cantonese - A Typical Chinese Dialect --- p.10Chapter 2.1.1 --- Cantonese Phonology --- p.11Chapter 2.1.2 --- Cantonese Phonetics --- p.12Chapter 2.2 --- Pronunciation Variation in Cantonese --- p.13Chapter 2.2.1 --- Phone Change and Sound Change --- p.14Chapter 2.2.2 --- Notation for Different Sound Units --- p.16Chapter 2.3 --- Summary --- p.17Reference --- p.18Chapter Chapter 3. --- Large-Vocabulary Continuous Speech Recognition for Cantonese --- p.19Chapter 3.1 --- Feature Representation of the Speech Signal --- p.20Chapter 3.2 --- Probabilistic Framework of ASR --- p.20Chapter 3.3 --- Hidden Markov Model for Acoustic Modeling --- p.21Chapter 3.4 --- Pronunciation Lexicon --- p.25Chapter 3.5 --- Statistical Language Model --- p.25Chapter 3.6 --- Decoding --- p.26Chapter 3.7 --- The Baseline Cantonese LVCSR System --- p.26Chapter 3.7.1 --- System Architecture --- p.26Chapter 3.7.2 --- Speech Databases --- p.28Chapter 3.8 --- Summary --- p.29Reference --- p.30Chapter Chapter 4. --- Pronunciation Model --- p.32Chapter 4.1 --- Pronunciation Modeling at Different Levels --- p.33Chapter 4.2 --- Phone-level pronunciation model and its Application --- p.35Chapter 4.2.1 --- IF Confusion Matrix (CM) --- p.35Chapter 4.2.2 --- Decision Tree Pronunciation Model (DTPM) --- p.38Chapter 4.2.3 --- Refinement of Confusion Matrix --- p.41Chapter 4.3 --- Summary --- p.43References --- p.44Chapter Chapter 5. --- Pronunciation Modeling at Lexical Level --- p.45Chapter 5.1 --- Construction of PVD --- p.46Chapter 5.2 --- PVD Pruning by Word Unigram --- p.48Chapter 5.3 --- Recognition Experiments --- p.49Chapter 5.3.1 --- Experiment 1 ´ؤPronunciation Modeling in LVCSR --- p.49Chapter 5.3.2 --- Experiment 2 ´ؤ Pronunciation Modeling in Domain Specific task --- p.58Chapter 5.3.3 --- Experiment 3 ´ؤ PVD Pruning by Word Unigram --- p.62Chapter 5.4 --- Summary --- p.63Reference --- p.64Chapter Chapter 6. --- Pronunciation Modeling at Acoustic Model Level --- p.66Chapter 6.1 --- Hierarchy of HMM --- p.67Chapter 6.2 --- Sharing of Mixture Components --- p.68Chapter 6.3 --- Adaptation of Mixture Components --- p.70Chapter 6.4 --- Combination of Mixture Component Sharing and Adaptation --- p.74Chapter 6.5 --- Recognition Experiments --- p.78Chapter 6.6 --- Result Analysis --- p.80Chapter 6.6.1 --- Performance of Sharing Mixture Components --- p.81Chapter 6.6.2 --- Performance of Mixture Component Adaptation --- p.84Chapter 6.7 --- Summary --- p.85Reference --- p.87Chapter Chapter 7. --- Pronunciation Modeling at Decoding Level --- p.88Chapter 7.1 --- Search Process in Cantonese LVCSR --- p.88Chapter 7.2 --- Model-Level Search Space Expansion --- p.90Chapter 7.3 --- State-Level Output Probability Modification --- p.92Chapter 7.4 --- Recognition Experiments --- p.93Chapter 7.4.1 --- Experiment 1 ´ؤModel-Level Search Space Expansion --- p.93Chapter 7.4.2 --- Experiment 2 ´ؤ State-Level Output Probability Modification …… --- p.94Chapter 7.5 --- Summary --- p.96Reference --- p.97Chapter Chapter 8. --- Conclusions and Suggestions for Future Work --- p.98Chapter 8.1 --- Conclusions --- p.98Chapter 8.2 --- Suggestions for Future Work --- p.100Reference --- p.103Appendix I Base Syllable Table --- p.104Appendix II Cantonese Initials and Finals --- p.105Appendix III IF confusion matrix --- p.106Appendix IV Phonetic Question Set --- p.112Appendix V CDDT and PCDT --- p.11

Models and analysis of vocal emissions for biomedical applications

This book of Proceedings collects the papers presented at the 3rd International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications, MAVEBA 2003, held 10-12 December 2003, Firenze, Italy. The workshop is organised every two years, and aims to stimulate contacts between specialists active in research and industrial developments, in the area of voice analysis for biomedical applications. The scope of the Workshop includes all aspects of voice modelling and analysis, ranging from fundamental research to all kinds of biomedical applications and related established and advanced technologies

The Chinese syllabic final : phonological relativity and constituent analysis

Cornell University East Asia Papers, No. 9, 211 p

Stress recognition from speech signal

Předložená disertační práce se zabývá vývojem algoritmů pro detekci stresu z řečového signálu. Inovativnost této práce se vyznačuje dvěma typy analýzy řečového signálu, a to za použití samohláskových polygonů a analýzy hlasivkových pulsů. Obě tyto základní analýzy mohou sloužit k detekci stresu v řečovém signálu, což bylo dokázáno sérií provedených experimentů. Nejlepších výsledků bylo dosaženo pomocí tzv. Closing-To-Opening phase ratio příznaku v Top-To-Bottom kritériu v kombinaci s vhodným klasifikátorem. Detekce stresu založená na této analýze může být definována jako jazykově i fonémově nezávislá, což bylo rovněž dokázáno získanými výsledky, které dosahují v některých případech až 95% úspěšnosti. Všechny experimenty byly provedeny na vytvořené české databázi obsahující reálný stres, a některé experimenty byly také provedeny pro anglickou stresovou databázi SUSAS.Presented doctoral thesis is focused on development of algorithms for psychological stress detection in speech signal. The novelty of this thesis aims on two different analysis of the speech signal- the analysis of vowel polygons and the analysis of glottal pulses. By performed experiments, the doctoral thesis uncovers the possible usage of both fundamental analyses for psychological stress detection in speech. The analysis of glottal pulses in amplitude domain according to Top-To-Bottom criterion seems to be as the most effective with the combination of properly chosen classifier, which can be defined as language and phoneme independent way to stress recognition. All experiments were performed on developed Czech real stress database and some observations were also made on English database SUSAS. The variety of possibly effective ways of stress recognition in speech leads to approach very high recognition accuracy of their combination, or of their possible usage for detection of other speaker’s state, which has to be further tested and verified by appropriate databases.