Data-Driven Techniques in Speech Synthesis. Questo articolo non è disponibile.Data-Driven Techniques in Speech Synthesis

Data-Driven Techniques in Speech Synthesis gives a first review of this new field. All areas of speech synthesis from text are covered, including text analysis, letter-to-sound conversion, prosodic marking and extraction of parameters to drive synthesis hardware.
Fuelled by cheap computer processing and memory, the fields of machine learning in particular and artificial intelligence in general are increasingly exploiting approaches in which large databases act as implicit knowledge sources, rather than explicit rules manually written by experts. Speech synthesis is one application area where the new approach is proving powerfully effective, the reliance upon fragile specialist knowledge having hindered its development in the past. This book provides the first review of the new topic, with contributions from leading international experts.
Data-Driven Techniques in Speech Synthesis is at the leading edge of current research, written by well respected experts in the field. The text is concise and accessible, and guides the reader through the new technology. The book will primarily appeal to research engineers and scientists working in the area of speech synthesis. However, it will also be of interest to speech scientists and phoneticians as well as managers and project leaders in the telecommunications industry who need an appreciation of the capabilities and potential of modern speech synthesis technology.

1. Learning About Speech from Data: Beyond NETtalk; R.I. Damper. 2. Constructing High-Accuracy Letter-to-Phoneme Rules with Machine Learning; G. Bakiri, Th.G. Dietterich. 3. Analogy, the Corpus and Pronunciation; K.P.H. Sullivan. 4. A Hierarchical Lexical Representation for Pronunciation Generation; H. Meng. 5. English Letter-Phoneme Conversion by Stochastic Transducers; R.W.P. Luk, R.I. Damper. 6. Selection of Multiphone Synthesis Units and Grapheme-to-Phoneme Transcription using Variable-Length Modeling of Strings; S. Deligne, et al. 7. TREETALK: Memory-Based Word Phonemisation; W. Daelemans, A. van den Bosch. 8. Learnable Phonetic Representations in a Connectionist TTS System - I: Text to Phonetics; A.D. Cohen. 9. Using the Tilt Intonation Model: A Data-Driven Approach; A.W. Black, et al. 10. Estimation of Parameters for the Klatt Synthesizer from a Speech Database; J. Coleman, A. Slater. 11. Training Accent and Phrasing Assignment on Large Corpora; J. Hirschberg. 12. Learnable Phonetic Representations in a Connectionist TTS System - II: Phonetics to Speech; A.D. Cohen.