Language identification using acoustic log-likelihoods of syllable-like units

Speech Communication

Volumn 48, Issue 8, 2006, Pages 913-926

  Nagarajan, T ^a   Murthy, H A ^a  

^a INDIAN INSTITUTE OF TECHNOLOGY MADRAS   (India)

Author keywords

Incremental training; Language identification; Syllable

Indexed keywords

ACOUSTIC SIGNAL PROCESSING; DATA REDUCTION; FORMAL LANGUAGES; MATHEMATICAL MODELS; SPEECH PROCESSING; SPEECH RECOGNITION;

AUTOMATIC SPOKEN LANGUAGE IDENTIFICATION (LID); INCREMENTAL TRAINING; PARALLEL PHONE RECOGNITION (PPR); SYLLABLE-LIKE UNITS;

SPEECH ANALYSIS;

EID: 33745354229     PISSN: 01676393     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.specom.2005.12.003     Document Type: Article

References (28)

1. Berkling, K.M., Arai, T., Bernard, E., 1994. Analysis of phoneme based features for language identification. In: Proceedings of IEEE Int. Conf. Acoust. Speech Signal Process., April 1994, pp. 289-292.
2. Fujimura O. Syllable as a unit of speech recognition. IEEE Trans. Acoust. Speech Signal Process. 23 1 (1975) 82-87
3. Godfrey, J.J., Holliman, E.C., McDaniel, J., 1992. SWITCHBOARD: telephone speech corpus for research and development. In: Proceedings of IEEE Int. Conf. Acoust. Speech and Signal Process., pp. 517-520.
4. Greenberg S. Speaking in short hand-a syllable-centric perspective for understanding pronunciation variation. Speech Comm. 29 (1999) 159-176
5. Hazen, T.J., Zue, V.W., 1994. Recent improvements in an approach to segment-based automatic language identification. In: Proceedings of IEEE Internat. Conf. Acoust. Speech Signal Process., September 1994, pp. 1883-1886.
6. Jayaram, A.K.V.S., Ramasubramanian, V., Sreenivas, T.V., 2003. Language identification using parallel sub-word recognition. In: Proceedings of IEEE Internat. Conf. Acoust. Speech Signal Process., vol. I, pp. 32-35.
7. Kadambe, S., Hieronymus, J.L., 1995. Language identification with phonological and lexical models. In: Proceedings of IEEE Internat. Conf. Acoust. Speech Signal Process., May 1995, pp. 3507-3510.
8. Lamel, L.F., Gauvain, J.L., 1994. Language identification using phone-based acoustic likelihoods. In: Proceedings of IEEE Internat. Conf. Acoust. Speech Signal Process., April 1994, vol. 1, pp. 293-296.
9. Li, K.P., 1994. Automatic language identification using syllabic spectral features. In: Proceedings of IEEE Internat. Conf. Acoust. Speech Signal Process., April 1994, pp. 297-300.
10. Mermelstein P. Automatic segmentation of speech into syllabic units. J. Acoust. Soc. Amer. 58 4 (1975) 880-883
11. Muthusamy, Y.K., Cole, R.A., Oshika, B.T., 1992. The OGI multilanguage telephone speech corpus. In: Proceedings of Internat. Conf. Spoken Language Process., October 1992, pp. 895-898.
12. Muthusamy Y.K., Barnard E., and Cole R.A. Reviewing automatic language identification. IEEE Signal Process. Mag. (1994) 33-41
13. Nagarajan, T., Murthy, H.A., 2004. Language identification using parallel syllable-like unit recognition. In: Proceedings of IEEE Internat. Conf. Acoust. Speech Signal Process., Montreal, Canada, May 2004, vol. 1, pp. 401-404.
14. Nagarajan, T., Murthy, H.A., Hegde, R.M., 2003. Segmentation of speech into syllable-like units. In: Proceedings of EUROSPEECH, Geneva, Switzerland, September 2003, pp. 2893-2896.
15. Nakagawa, S., Hashimoto, Y., 1988. A method for continuous speech segmentation using hmm. In: Proceedings of IEEE Internat. Conf. Acoust. Speech Signal Process., pp. 960-962.
16. Navratil, J., Zuhlke, W., 1997. Phonetic-context mapping in language identification. In: Proceedings of EUROSPEECH, Greece, September 1997, vol. 1, pp. 71-74.
17. Noetzel, A., 1991. Robust syllable segmentation of continuous speech using neural networks. In: Electro International, pp. 580-585.
18. Prasad, V.K., 2003. Segmentation and Recognition of Continuous Speech. Ph.D. Dissertation, Indian Institute of Technology, Department of Computer Science and Engineering, Madras, India.
19. Prasad V.K., Nagarajan T., and Murthy H.A. Automatic segmentation of continuous speech using minimum phase group delay functions. Speech Comm. 42 (2004) 429-446
20. Rabiner L.R., and Schafer R.W. Digital Processing of Speech Signals (1978), Prentice Hall, New Jersy
21. Ramasubramanian, V., Jayaram, A.K.V.S., Sreenivas, T.V., 2003. Language identification using parallel phone recognition. In: WSLP, TIFR, Mumbai, January 2003, pp. 109-116.
22. Ramasubramanian, V., Jayaram, A.K.V.S., Sreenivas, T.V., 2003. Language identification using parallel sub-word recognition-an ergodic hmm equivalence. In: Proceedings of EUROSPEECH, September 2003, pp. 1357-1360.
23. Schmidbauer, O., 1987. Syllable-based segment-hypotheses generation in fluently spoken speech using gross articulatory features. In: Proceedings of IEEE Internat. Conf. Acoust. Speech Signal Process., pp. 391-394.
24. Shastri, L., Chang, S., Greenberg, S., 1999. Syllable detection and segmentation using temporal flow neural networks. In: ICPhS, San Francisco, pp. 1721-1724.
25. Singer, E., Torres-Carrasquillo, P.A., Gleason, T.P., Campbell, W.M., Reynolds, D.A., 2003. Acoustic, phonetic, and discriminative approaches to automatic language identification. In: Proceedings of EUROSPEECH, Geneva, September 2003, pp. 1345-1348.
26. Wu, S.L., Kingsbury, E.D., Morgan, N., Greenberg, S., 1998. Incorporating information from syllable-length time scales into automatic speech recognition. In: Proceedings of IEEE Internat. Conf. Acoust. Speech Signal Process., Seatle, WA, May 1998, pp. 721-724.
27. Yan, Y., Barnard, E., 1995. A approach to automatic langauge identification based on language-dependent phone recognition. In: Proceedings of IEEE Internat. Conf. Acoust. Speech Signal Process., May 1995, pp. 3511-3514.
28. Zissman M.A. Comparison of four approaches to automatic language identification of telephone speech. IEEE Trans. Speech Audio Process. 4 1 (1996) 31-44