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. Language: English