Text to Speech Synthesis
New Paradigms and Advances
Shrikanth Narayanan, Abeer Alwan
Résumé
Recent advances in speech synthesis will enable the development of high-quality natural voice systems with broad application in education, business, entertainment, and medicine. Text to Speech Synthesis is the first book to comprehensively document these new research trends and paradigms, balancing coverage of research and applications. It brings together seminal research by leaders in the field, drawn from both academic and industrial laboratories worldwide.
The authors and editors offer broad coverage of several key areas, including new unit selection approaches, speech representations and modeling, data-driven synthesis schemes, and expressive speech synthesis.
Coverage includes:
- Unit Selection Methods: Reducing discontinuities at synthesis time in corpus-based speech processing, voice quality variation, and join costs
- Hidden Markov Model (HMM)-Based Synthesis: Advanced uses of speech recognition technology, HMM-based multilingual speech synthesis, and new prosody control techniques
- Expressive Speech Synthesis: Challenges, questions, and avenues of research, including diphone transplantation and minimization of pitch modification
- Speech Representation and Models: A new articulatory modeling paradigm for controlling synthesis quality
This is an essential resource for all researchers working in speech synthesis and related areas such as multimedia signal processing, linguistics, and spoken user interfaces. It will also be valuable to any engineer, developer, or manager who must evaluate the latest speech technologies or integrate them into practical applications.
L'auteur - Shrikanth Narayanan
Dr. Shrikanth Narayanan is associate professor at the Signal and Image Processing Institute of USC's Electrical Engineering Department. He founded and directs USC's Speech Analysis and Interpretation Laboratory, and serves as research area director of the Integrated Media Systems Center, an NSF Engineering Research Center. He is associate editor of IEEE Transactions of Speech and Audio Processing, serves on the speech communication technical committee of the Acoustical Society of America, and was Principal Member of Technical Staff at AT&T Laboratories.
L'auteur - Abeer Alwan
Dr. Abeer Alwan, a professor of electrical engineering at UCLA, established and directs the Speech Processing and Auditory Perception Laboratory there. Her research interests include modeling human speech production and perception mechanisms and applying these models to speech-processing applications such as noise-robust automatic speech recnognition, compression, and synthesis. She is a Fellow of the Acoustical Society of America and recently served as editor-in-chief of the journal Speech Communication.
Sommaire
- Reducing Discontinuities at Synthesis Time for Corpus-Based Speech Synthesis
- Voice Quality Variation in a Long-Term Recording of a Single Speaker Speech Corpus
- Join Cost for Unit Selection Speech Synthesis
- Articulatory Modeling: A Role in Concatenative Text to Speech Synthesis
- Minimizing The Amount of Pitch Modification in Speech Synthesis
- The Use of Speech Recognition Technology in Speech Synthesis
- An HMM-Based Approach to Multilingual Speech Synthesis
- Prosody Control For HMM-Based Japanese TTS
- Synthesizing Expressive Speech Overview: Challenges, and Open Questions
- Unit Selection Synthesis of Prosody: Evaluation Using Diphone Transplantation
- Toward Expressive Synthetic Speech
Caractéristiques techniques
| PAPIER | |
| Éditeur(s) | Prentice Hall |
| Auteur(s) | Shrikanth Narayanan, Abeer Alwan |
| Parution | 23/08/2004 |
| Nb. de pages | 260 |
| Format | 18,5 x 24 |
| Couverture | Relié |
| Poids | 765g |
| Intérieur | Noir et Blanc |
| EAN13 | 9780131456617 |
| ISBN13 | 978-0-131-45661-7 |
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