Data-Driven Techniques in Speech Synthesis

1 Learning About Speech from Data: Beyond NETtalk.- 1.1 Introduction.- 1.2 Architecture of a TTS System.- 1.3 Automatic Pronunciation Generation.- 1.4 Prosody.- 1.5 The Synthesis Module.- 1.6 Conclusion.- 2 Constructing High-Accuracy Letter-to-Phoneme Rules with Machine Learning.- 2.1 Introduction.- 2.2 The Nettalk Approach.- 2.3 High-Performance ML Approach.- 2.4 Evaluation of Pronunciations.- 2.5 Conclusions.- 3 Analogy, the Corpus and Pronunciation.- 3.1 Introduction.- 3.2 Why Adopt a Psychological Approach?.- 3.3 The Corpus as a Resource.- 3.4 The Sullivan and Damper Model.- 3.5 Parallels with Optimality Theory.- 3.6 Implementation.- 3.7 Corpora.- 3.8 Performance Evaluation.- 3.9 Future Challenges.- 4 A Hierarchical Lexical Representation for Pronunciation Generation.- 4.1 Introduction.- 4.2 Previous Work.- 4.3 Hierarchical Lexical Representation.- 4.4 Generation Algorithm.- 4.5 Evaluation Criteria.- 4.6 Results on Letter-to-Sound Generation.- 4.7 Error Analyses.- 4.8 Evaluating the Hierarchical Representation.- 4.9 Discussions and Future Work.- 5 English Letter-Phoneme Conversion by Stochastic Transducers.- 5.1 Introduction.- 5.2 Modelling Transduction.- 5.3 Stochastic Finite-State Transducers.- 5.4 Inference of Letter-Phoneme Correspondences.- 5.5 Translation.- 5.6 Results.- 5.7 Conclusions.- 6 Selection of Multiphone Synthesis Units and Grapheme-to-Phoneme Transcription using Variable-Length Modeling of Strings.- 6.1 Introduction.- 6.2 Multigram Model.- 6.3 Multiphone Units for Speech Synthesis.- 6.4 Learning Letter-to-Sound Correspondences.- 6.5 General Discussion and Perspectives.- 7 TreeTalk: Memory-Based Word Phonemisation.- 7.1 Introduction.- 7.2 Memory-Based Phonemisation.- 7.3 tribl and TreeTalk.- 7.4 Modularity and Linguistic Representations.- 7.5 Conclusion.- 8 Learnable Phonetic Representations in a Connectionist TTS System — I: Text to Phonetics.- 8.1 Introduction.- 8.2 Problem Background.- 8.3 Data Inputs and Outputs to Module M1.- 8.4 Detailed Architecture of the Text-to-Phonetics Module.- 8.5 Model Selection.- 8.6 Results.- 8.7 Conclusions and Further Work.- 9 Using the Tilt Intonation Model: A Data-Driven Approach.- 9.1 Background.- 9.2 Tilt Intonation Model.- 9.3 Training Tilt Models.- 9.4 Experiments and Results.- 9.5 Conclusion.- 10 Estimation of Parameters for the Klatt Synthesizer from a Speech Database.- 10.1 Introduction.- 10.2 Global Parameter Settings.- 10.3 Synthesis of Vowels, Diphthongs and Glides.- 10.4 Stop Consonants (and Voiceless Vowels).- 10.5 Estimation of Fricative Parameters.- 10.6 Other Sounds.- 10.7 Application: A Database of English Monosyllables.- 10.8 Conclusion.- 11 Training Accent and Phrasing Assignment on Large Corpora.- 11.1 Introduction.- 11.2 Intonational Model.- 11.3 Classification and Regression Trees.- 11.4 Predicting Pitch Accent Placement.- 11.5 Predicting Phrase Boundary Location.- 11.6 Conclusion.- 12 Learnable Phonetic Representations in a Connectionist TTS System — II: Phonetics to Speech.- 12.1 Introduction.- 12.2 Architecture of Phonetics-to-Speech Module.- 12.3 Training and Alignment.- 12.4 Phonetics-to-Speech Results.- 12.5 Conclusions and Further Work.