Application of prosody models for developing speech systems in Indian languages

International Journal of Speech Technology

Volumn 14, Issue 1, 2011, Pages 19-33

  Rao, K Sreenivasa ^a  

^a INDIAN INSTITUTE OF TECHNOLOGY   (India)

Author keywords

Duration; Feedforward neural network; Intonation; Prosody; Speech systems

Indexed keywords

DURATION; FEED-FORWARD; INTONATION; PROSODY; SPEECH SYSTEMS;

FEEDFORWARD NEURAL NETWORKS; SPEECH SYNTHESIS;

SPEECH RECOGNITION;

EID: 79953168002     PISSN: 13812416     EISSN: 15728110     Source Type: Journal    
DOI: 10.1007/s10772-010-9086-9     Document Type: Article

References (48)

1. Batliner, A., Mobius, B., Mohler, G., Schweitzer, A., & Noth, E. (2001). Prosodic models, automatic speech understanding, and speech synthesis: towards the common ground. In Eurospeech, Scandinavia.
2. Benesty, J., Sondhi, M. M., & Huang, Y. (Eds.) (2008). Springer handbook on speech processing. New York: Springer.
3. Bozkurt, B., Bagein, M., & Dutoit, T. (2001). From MBROLA to NU-MBROLA. In Proc. 4th ISCA workshop on speech synthesis, Pitlochry, Scotland, UK (pp. 127-129).
4. Bozkurt, B., Dutoit, T., Prudon, R., D'Alessandro, C., & Pagel, V. (2002). Improving quality of MBROLA synthesis for non-uniform units synthesis. In IEEE workshop on speech synthesis, Santa Monica, California, USA, Sept. 2002.
5. Chopde, A. (2009). Itrans Indian language transliteration package version 5.2 source. http://www.aczone.com/itrans/.
6. Deller, J. R., Proakis, J. G., & Hansen, J. H. L. (1993). Discrete-time processing of speech signals. New York: Macmillan Co.
7. Gangashetty, S. V., Sekhar, C. C., & Yegnanarayana, B. (2004). Acoustic model combination for recognition of speech in multiple languages using support vector machines. In Proc. IEEE int. conf. acoust., speech, signal processing.
8. Gangashetty, S. V., Sekhar, C. C., & Yegnanarayana, B. (2004). Extraction of fixed dimension patterns from varying duration segments of consonant-vowel utterances. In Proc. IEEE int. conf. intelligent sensing and information processing, Chennai, India, Jan. 2004 (pp. 159-164).
9. Haykin, S. (1999). Neural networks: a comprehensive foundation. Upper Saddle River: Pearson Education.
10. Hogg, R. V., & Ledolter, J. (1987). Engineering statistics. New York: Macmillan Co.
11. Huang, X., Acero, A., & Hon, H. W. (2001). Spoken language processing. New York: Prentice-Hall.
12. Hwang, S.-H., & Chen, S.-H. (1995). A prosodic model for mandarin speech and its application to pitch level generation for text-to-speech. In Proc. IEEE int. conf. acoust., speech, signal processing, May 1995 (pp. 616-619).
13. Khan, A. N., Gangashetty, S. V., & Yegnanarayana, B. (2003). Syllabic properties of three Indian languages: Implications for speech recognition and language identification. In Int. conf. natural language processing, Mysore, India, Dec. 2003 (pp. 125-134).
14. Kishore, S. P., Kumar, R., & Sangal, R. (2002). A data-driven synthesis approach for Indian languages using syllable as basic unit. In Int. conf. natural language processing.
15. Krishna, N. S., Murthy, H. A., & Gonsalves, T. A. (2002). Text-to-speech (tts) in Indian languages. In Int. conf. natural language processing.
16. Kumar, S. R. R. (1990). Significance of durational knowledge for a text-to-speech system in an Indian language. Master's thesis, Dept. of computer science and engineering, indian institute of technology, Madras, March.
17. A. S. M. Kumar S. Rajendran B. Yegnanarayana 1993 Intonation component of text-to-speech system for Hindi Computer Speech and Language 7 283 301 10.1006/csla.1993.1015 (Pubitemid 23705304)
18. Kumar, K. K., Rao, K. S., & Yegnanarayana, B. (2002). Duration knowledge for text-to-speech system for Telugu. In Proc. int. conf. knowledge based computer systems, Mumbai, India, Dec. 2002 (pp. 563-571).
19. Lee, S., Hirose, K., & Minematsu, N. (2001). Incorporation of prosodic modules for large vocabulary continuous speech recognition. In Proc. ISCA workshop on prosody in speech recognition and understanding (pp. 97-101).
20. Leung, C.-C., Ferras, M., Barras, C., & Gauvain, J.-L. (2008). Comparing prosodic models for speaker recognition. In Interspeech, Brisbane, Australia, Sept 2008 (pp. 1945-1948).
21. Mary, L. (2006). Multi level implicit features for language and speaker recognition. PhD thesis, Dept. of Computer Science and Engineering, Indian Institute of Technology Madras, Chennai, India, June.
22. L. Mary B. Yegnanarayana 2008 Extraction and representation of prosodic features for language and speaker recognition Speech communication 50 782 796 10.1016/j.specom.2008.04.010
23. P. S. Murthy B. Yegnanarayana 1999 Robustness of group-delay-based method for extraction of significant excitation from speech signals IEEE Transactions on Speech and Audio Processing 7 609 619 10.1109/89.799686
24. Ostendorfy, M., Shafranz, I., & Bates, R. (2003). Prosody models for conversational speech recognition. In Symposium on prosody and speech.
25. Prasanna, S. R. M. (2004). Event-based analysis of speech. PhD thesis, Dept. of computer science and engineering, Indian institute of technology, Madras, Chennai, India, March.
26. Prasanna, S. R. M., & Yegnanarayana, B. (2004). Extraction of pitch in adverse conditions. In Proc. IEEE int. conf. acoust., speech, signal processing, Montreal, Canada, May 2004.
27. Prasanna, S. R. M., & Zachariah, J. M. (2002). Detection of vowel onset point in speech. In Proc. IEEE int. conf. acoust., speech, signal processing, Orlando, Florida, USA, May 2002.
28. S. R. M. Prasanna B. V. S. Reddy P. K. Murthy 2009 Vowel onset point detection using source, spectral peaks, and modulation spectrum energies IEEE Transactions on Speech and Audio Processing 17 556 565 10.1109/TASL.2008.2010884
29. Rabiner, L. R., & Juang, B. H. (1993). Fundamentals of speech recognition. Prentice Hall: Englewood Cliffs.
30. Rajendran, S., Rao, K. S., Yegnanarayana, B., & Reddy, K. N. (2003). Syllable duration in broadcast news in Telugu: A preliminary study. In National conf. language technology tools: implementation of telugu/urdu, Hyderabad, India, Oct. 2003.
31. Rao, K. S. (2008). Modeling supra-segmental features of syllables using neural networks. In P. B. Prasad & S. R. M. Prasanna (Eds.), Speech, audio, image and biomedical signal processing using neural networks (pp. 71-95). New York: Springer.
32. Rao, K. S., & Yegnanarayana, B. (2003). Prosodic manipulation using instants of significant excitation. In Proc. IEEE int. conf. multimedia and expo, Baltimore, Maryland, USA, July 2003 (pp. 389-392).
33. K. S. Rao B. Yegnanarayana 2006 Prosody modification using instants of significant excitation IEEE Transactions on Speech and Audio Processing 14 972 980 10.1109/TSA.2005.858051 (Pubitemid 46547658)
34. K. S. Rao B. Yegnanarayana 2007 Modeling durations of syllables using neural networks Computer Speech and Language 21 282 295 10.1016/j.csl.2006.06. 003 (Pubitemid 44709836)
35. K. S. Rao B. Yegnanarayana 2009 Intonation modeling for Indian languages Computer Speech and Language 23 240 256 10.1016/j.csl.2008.06.005
36. Shriberg, E., & Stolcke, A. (2001). Prosody modeling for automatic speech understanding: An overview of recent research at SRI. In Prosody in speech recognition and understanding, ISCA tutorial and research workshop (ITRW), Molly Pitcher Inn, Red Bank, NJ, USA, Oct. 2001.
37. Shriberg, E., & Stolcke, A. (2004). Mathematical foundations of speech and language processing. New York: Springer.
38. R. Smits B. Yegnanarayana 1995 Determination of instants of significant excitation in speech using group delay function IEEE Transactions on Speech and Audio Processing 3 325 333 10.1109/89.466662
39. Srikanth, S., Kumar, S. R. R., Sundar, R., & Yegnanarayana, B. (1989). A text-to-speech conversion system for Indian languages based on waveform concatenation model. Technical report No. 11, project VOIS, Dept. of computer science and engineering, Indian institute of technology, Madras, March.
40. Stevens, K. N. (1999). Acoustic phonetics. Cambridge: MIT Press.
41. Suryakanth, G. V. (2005). Neural network models for recognition of consonant-vowel units of speech in multiple languages. PhD thesis, Dept. of computer science and engineering, Indian institute of technology, Madras, Chennai, India, May.
42. Vainio, M. (2001). Artificial neural network based prosody models for Finnish text-to-speech synthesis. PhD thesis, Dept. of phonetics, University of Helsinki, Finland.
43. Vainio, M., & Altosaar, T. (1998). Modeling the microprosody of pitch and loudness for speech synthesis with neural networks. In Proc. int. conf. spoken language processing, Sidney, Australia, Dec. 1998.
44. Vapnik, V. N. (2001). Statistical learning theory. New York: Wiley.
45. Weber, F., Manganaro, L., Peskin, B., & Shriberg, E. (2002). Using prosodic and lexical information for speaker identification. In Proc. IEEE int. conf. acoust., speech, signal processing.
46. Werner, S., & Keller, E. (1994). Prosodic aspects of speech. In E. Keller (Ed.), Fundamentals of speech synthesis and speech recognition: basic concepts, state of the art, the future challenges (pp. 23-40). Chichester: Wiley.
47. Yegnanarayana, B. (1999). Artificial neural networks. New York: Prentice Hall.
48. Yin, B., Ambikairajah, E., & Chen, F. (2006). Combining cepstral and prosodic features in language identification. In 18th international conference on pattern recognition (ICPR'06).