Effect of speech-intrinsic variations on human and automatic recognition of spoken phonemes

Journal of the Acoustical Society of America

Volumn 129, Issue 1, 2011, Pages 388-403

  Meyer, Bernd T ^a   Brand, Thomas ^a   Kollmeier, Birger ^a  

^a UNIVERSITY OF OLDENBURG   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

AUTOMATIC RECOGNITION; AUTOMATIC SPEECH RECOGNITION SYSTEM; ERROR RATE; HUMAN LISTENERS; HUMAN PERFORMANCE; HUMAN-MACHINE; INTRINSIC VARIABILITIES; NOISY ENVIRONMENT; PHONEME RECOGNITION; RECOGNITION PERFORMANCE; RECOGNITION RATES; SIGNAL TO NOISE; SPEAKING RATE; TEMPORAL CUES; TEMPORAL DYNAMICS;

CONTINUOUS SPEECH RECOGNITION; SIGNAL TO NOISE RATIO; SPEECH;

FEATURE EXTRACTION;

ARTICLE; ARTIFICIAL NEURAL NETWORK; ASSOCIATION; AUDITORY STIMULATION; AUDITORY THRESHOLD; AUTOMATED PATTERN RECOGNITION; AUTOMATIC SPEECH RECOGNITION; COMPARATIVE STUDY; FEMALE; HUMAN; MALE; NOISE; PATTERN RECOGNITION; PERCEPTION; PHONETICS; PURE TONE AUDIOMETRY; RECOGNITION; SPEECH; SPEECH AUDIOMETRY; SPEECH PERCEPTION; TIME; TIME PERCEPTION;

ACOUSTIC STIMULATION; AUDIOMETRY, PURE-TONE; AUDIOMETRY, SPEECH; AUDITORY THRESHOLD; CUES; FEMALE; HUMANS; MALE; NEURAL NETWORKS (COMPUTER); NOISE; PATTERN RECOGNITION, AUTOMATED; PATTERN RECOGNITION, PHYSIOLOGICAL; PERCEPTUAL MASKING; PHONETICS; RECOGNITION (PSYCHOLOGY); SPEECH ACOUSTICS; SPEECH PERCEPTION; SPEECH RECOGNITION SOFTWARE; TIME FACTORS; TIME PERCEPTION;

EID: 79551679242     PISSN: 00014966     EISSN: None     Source Type: Journal    
DOI: 10.1121/1.3514525     Document Type: Article

References (34)

1. Barker, J., and Cooke, M. (2007). Modelling speaker intelligibility in noise., Speech Commun. 49, 402-417. 10.1016/j.specom.2006.11.003 (Pubitemid 46670363)
2. Benzeguiba, M., De Mori, R., Deroo, O., Dupont, S., Erbes, T., Jouvet, D., Fissore, L, Laface, P., Mertins, A., Ris, C., Rose, R., Tyagi, V., and Wellekens, C. (2007). Automatic speech recognition and speech variability: A review., Speech Commun. 49, 763-786. 10.1016/j.specom.2007.02.006 (Pubitemid 47268571)
3. Bronkhorst, A. W., Bosman, A. J., and Smoorenburg, G. F. (1993). A model for context effects in speech recognition., J. Acoust. Soc. Am. 93, 499-509. 10.1121/1.406844
4. Carey, M. J., and Quang, T. P. (2005). A speech similarity distance weighting for robust recognition., in Proceedings of Interspeech, Lisbon, Portugal, pp. 1257-1260.
5. Cooke, M. (2005). A glimpsing model of speech perception in noise., J. Acoust. Soc. Am. 119, 1562-1573. 10.1121/1.2166600
6. Cooke, M., and Scharenborg, O. (2008). The Interspeech 2008 consonant challenge., in Proceedings of Interspeech, pp. 1781-1784.
7. Davis, S., and Mermelstein, P. (1980). Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences., IEEE Trans. Acoust. Speech. Signal Process. 28, 357-366. 10.1109/TASSP.1980.1163420
8. Demuynck, K., Garcia, O., and Van Compernolle, D. (2004). Synthesizing speech from speech recognition parameters., in Proceedings of Interspeech, pp. II-945-II-948.
9. Dreschler, W. A., Verschuure, H., Ludvigson, C, and Westermann, S. (2001). ICRA noises: Artificial noise signals with speech-like spectral and temporal properties for hearing instrument assessment., Int. J. Audiol. 40, 148-157. 10.3109/00206090109073110
10. Gillick, L., and Cox, S. J. (1989). Some statistical issues in the comparison of speech recognition algorithms., in Proceedings of the 1989 International Conference on Acoustics, Speech, and Signal Processing, Glasgow, United Kingdom, pp. 532-535.
11. Griffin, D., and Lim, J. (1984). Signal estimation from modified short-time Fourier transform., IEEE Trans. Acoust. Speech. Signal Process. 32, 236-243. 10.1109/TASSP.1984.1164317 (Pubitemid 14608418)
12. Hawkins, S. (2003). Roles and representations of systematic fine phonetic detail in speech understanding., J. Phonetics 31, 373-405. 10.1016/j.wocn.2003. 09.006 (Pubitemid 37495914)
13. Hermansky, H., and Sharma, S. (1999). Temporal patterns (TRAPS) in ASR of noisy speech., in Proceedings of the 1999 International Conference on Acoustics, Speech, and Signal Processing, Phoenix, Arizona, pp. 289-292.
14. Hillenbrand, J., Getty, L., Clark, M., and Wheeler, K. (1995). Acoustic characteristics of American English vowels., J. Acoust. Soc. Am. 97, 3099-3111. 10.1121/1.411872
15. Junqua, J. -C. (1993). The Lombard reflex and its role on human listeners and automatic speech recognizers., J. Acoust. Soc. Am. 93, 510-524. 10.1121/1.405631
16. Jrgens, T., Brand, T., and Kollmeier, B. (2009). Predicting consonant recognition in quiet for listeners with normal hearing and hearing impairment using an auditory model (A)., J. Acoust. Soc. Am. 125, 2533.
17. Kipp, A., Wesenick, M., and Schiel, F. (1996). Automatic detection and segmentation of pronunciation variants in German speech corpora., in Proceedings of the 1996 International Conference on Spoken Language Processing, Philadelphia, Pennsylvania, pp. 106-109.
18. Kohler, K. (1995). Einfhrung in die Phonetik des Deutschen (Introduction to German phonetics) (Erich Schmidt Verlag, Berlin), pp. 1-249.
19. Krause, J. C., and Braida, L. D. (1995). The effects of speaking rate on the intelligibility of speech for various speaking modes (A)., J. Acoust. Soc. Am. 98, 2982. 10.1121/1.413900
20. Krause, J. C., and Braida, L. D. (2003). Acoustic properties of naturally produced clear speech at normal speaking rates., J. Acoust. Soc. Am. 115, 362-378. 10.1121/1.1635842
21. Leonard, R. (1984). A database for speaker-independent digit recognition., in Proceedings of the 1984 International Conference on Acoustics, Speech, and Signal Processing, Vol. IX, pp. 328-331.
22. Lippmann, R. (1997). Speech recognition by machines and humans., Speech Commun. 22, 1-15. 10.1016/S0167-6393(97)00021-6
23. Meyer, B., and Wesker, T. (2006). A human-machine comparison in speech recognition based on a logatome corpus., in Workshop on Speech-Intrinsic Variation, pp. 95-100.
24. Miller, G. A., and Licklider, J. (1950). The intelligibility of interrupted speech., J. Acoust. Soc. Am. 22, 167-173. 10.1121/1.1906584
25. Miller, G. A., and Nicely, P. E. (1955). An analysis of perceptual confusions among some English consonants., J. Acoust. Soc. Am. 27, 338-352. 10.1121/1.1907526
26. Peters, S., Stubley, P., and Valin, J. (1999). On the limits of speech recognition in noise., in Proceedings of the 1999 International Conference on Acoustics, Speech, and Signal Processing, Phoenix, Arizona, pp. 365-368.
27. Phatak, S. A., and Allen, J. B. (2007). Consonant and vowel confusions in speech-weighted noise., J. Acoust. Soc. Am. 121, 2312-2326. 10.1121/1.2642397 (Pubitemid 46548430)
28. Scharenborg, O. (2007). Reaching over the gap: A review of efforts to link human and automatic speech recognition research., Speech Commun. 49, 336-347. 10.1016/j.specom.2007.01.009 (Pubitemid 46670364)
29. Schlter, R., and Ney, H. (2001). Using phase spectrum information for improved speech recognition performance., in Proceedings of the 2001 International Conference on Acoustics, Speech, and Signal Processing, Salt Lake City, Utah, pp. 133-136.
30. Shen, W., Olive, J., and Jones, D. (2008). Two protocols comparing human and machine phonetic recognition performance in conversational speech., in Proceedings of Interspeech, pp. 1630-1633.
31. Shriberg, L. D., Kwiatkowski, J., and Hoffmann, K. (1984). A procedure for phonetic transcription by consensus., J. Speech Lang. Hear. Res. 27, 456-465.
32. Sroka, J. J., and Braida, L. D. (2005). Human and machine consonant recognition., Speech Commun. 45, 401-423. 10.1016/j.specom.2004.11.009 (Pubitemid 40423287)
33. Stern, R., Acero, A., Liu, F. H., and Ohshima, Y. (1996). Signal processing for robust speech recognition., in Automatic Speech and Speaker Recognition, edited by, C. -H. Lee, F. K. Soong, and, K. K. Paliwal, (Springer, Berlin), pp. 357-384.
34. Wesker, T., Meyer, B., Wagener, K., Anemueller, J., Mertins, A., and Kollmeier, B. (2005). Oldenburg Logatome Speech Corpus (OLLO) for speech recognition experiments with humans and machines., in Proceedings of Interspeech, pp. 1273-1276.