Predicting Prosody from Text for Text-to-Speech Synthesis
Predicting Prosody from Text for Text-to-Speech Synthesis covers the specific aspects of prosody, mainly focusing on how to predict the prosodic information from linguistic text, and then how to exploit the predicted prosodic knowledge for various speech applications. Author K. Sreenivasa Rao discus...
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| Format: | Electronic eBook |
| Language: | English |
| Published: |
New York, NY :
Springer New York : Imprint: Springer,
2012.
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| Edition: | 1st ed. 2012. |
| Series: | SpringerBriefs in Speech Technology, Studies in Speech Signal Processing, Natural Language Understanding, and Machine Learning,
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| Subjects: | |
| Online Access: | https://doi.org/10.1007/978-1-4614-1338-7 |
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|---|---|---|---|
| 001 | 978-1-4614-1338-7 | ||
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| 100 | 1 | |a Rao, K. Sreenivasa. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 245 | 1 | 0 | |a Predicting Prosody from Text for Text-to-Speech Synthesis |h [electronic resource] / |c by K. Sreenivasa Rao. |
| 250 | |a 1st ed. 2012. | ||
| 264 | 1 | |a New York, NY : |b Springer New York : |b Imprint: Springer, |c 2012. | |
| 300 | |a XII, 130 p. 42 illus. |b online resource. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 347 | |a text file |b PDF |2 rda | ||
| 490 | 1 | |a SpringerBriefs in Speech Technology, Studies in Speech Signal Processing, Natural Language Understanding, and Machine Learning, |x 2191-737X | |
| 505 | 0 | |a 1. Introduction -- 2. Prosody Knowledge for Speech Systems: A Review -- 3. Analysis of Durationsn of Sound Units -- 4. Modeling Duration -- 5. Modeling Intonation -- 6. Prosody Modification -- 7. Practical Aspects of Prosody Modification -- 8. Summary and Conclusions -- Appendix A. Coding Scheme Used to Represent Linguistic and Production Constraints. | |
| 520 | |a Predicting Prosody from Text for Text-to-Speech Synthesis covers the specific aspects of prosody, mainly focusing on how to predict the prosodic information from linguistic text, and then how to exploit the predicted prosodic knowledge for various speech applications. Author K. Sreenivasa Rao discusses proposed methods along with state-of-the-art techniques for the acquisition and incorporation of prosodic knowledge for developing speech systems. Positional, contextual and phonological features are proposed for representing the linguistic and production constraints of the sound units present in the text. This book is intended for graduate students and researchers working in the area of speech processing. | ||
| 650 | 0 | |a Signal processing. | |
| 650 | 0 | |a Image processing. | |
| 650 | 0 | |a Speech processing systems. | |
| 650 | 0 | |a Computational linguistics. | |
| 650 | 0 | |a Natural language processing (Computer science). | |
| 650 | 1 | 4 | |a Signal, Image and Speech Processing. |0 https://scigraph.springernature.com/ontologies/product-market-codes/T24051 |
| 650 | 2 | 4 | |a Computational Linguistics. |0 https://scigraph.springernature.com/ontologies/product-market-codes/N22000 |
| 650 | 2 | 4 | |a Natural Language Processing (NLP). |0 https://scigraph.springernature.com/ontologies/product-market-codes/I21040 |
| 710 | 2 | |a SpringerLink (Online service) | |
| 773 | 0 | |t Springer Nature eBook | |
| 776 | 0 | 8 | |i Printed edition: |z 9781461413394 |
| 776 | 0 | 8 | |i Printed edition: |z 9781461413370 |
| 830 | 0 | |a SpringerBriefs in Speech Technology, Studies in Speech Signal Processing, Natural Language Understanding, and Machine Learning, |x 2191-737X | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/978-1-4614-1338-7 |
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| 950 | |a Engineering (SpringerNature-11647) | ||
| 950 | |a Engineering (R0) (SpringerNature-43712) | ||