Modeling coarticulation in EMG-based continuous speech recognition

Speech Communication

Volumn 52, Issue 4, 2010, Pages 341-353

  Schultz, Tanja ^a   Wand, Michael ^a  

^a KARLSRUHE INSTITUTE OF TECHNOLOGY   (Germany)

Author keywords

EMG based speech recognition; Phonetic features; Silent Speech Interfaces

Indexed keywords

AMBIENT NOISE; AUTOMATIC SPEECH RECOGNITION; BASELINE SYSTEMS; CO-ARTICULATION; CONFIDENTIAL INFORMATION; ELECTROMYOGRAPHIC SIGNAL; ENVIRONMENTAL NOISE; FACIAL MUSCLES; LARGE VOCABULARY; NEW APPROACHES; PHONETIC FEATURES; SPEECH INTERFACE; SPEECH RECORDING; SPEECH SIGNALS; SURFACE ELECTROMYOGRAPHY; WORD ERROR RATE;

CONTINUOUS SPEECH RECOGNITION; FEATURE EXTRACTION; SPEECH PROCESSING;

LINGUISTICS;

EID: 76849099234     PISSN: 01676393     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.specom.2009.12.002     Document Type: Article

References (27)

1. Bahl, L.R., de Souza, P.V., Gopalakrishnan, P.S., Nahamoo, D., Picheny, M.A., 1991. Decision trees for phonological rules in continuous speech. In: Proc. IEEE Internat. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), Toronto, Ontario, Canada, pp. 185-188.
2. Beyerlein, P., 2000. Diskriminative Modellkombination in Spracherkennungssystemen mit großem Wortschatz, Ph.D. Thesis. RWTH Aachen.
3. Chan A., Englehart K., Hudgins B., and Lovely D. Myoelectric signals to augment speech recognition. Med. Biological Eng. Comput. 39 (2001) 500-506
4. Denby B., Schultz T., Honda K., Hueber T., and Gilbert J. Silent speech interfaces. Speech Comm. 52 4 (2010) 270-287
5. Dietrich, M., 2008. The Effects of Stress Reactivity on Extralaryngeal Muscle Tension in Vocally Normal Participants as a Function of Personality, Ph.D. Thesis. University of Pittsburgh.
6. Frankel, J., Wester, M., King, S., 2004. Articulatory feature recognition using dynamic Bayesian networks. In: Proc. Internat. Conf. on Spoken Language Processing (ICSLP), Jeju Island, Korea, pp. 1202-1205.
7. International Phonetic Association. Handbook of the International Phonetic Association (1999), Cambridge University Press
8. Jorgensen, C., Lee, D., Agabon, S., 2003. Sub auditory speech recognition based on EMG/EPG signals. In: Proc. Internat. Joint Conf. on Neural Networks (IJCNN), Portland, Oregon, pp. 3128-3133.
9. Jou, S.-C., Schultz, T., Walliczek, M., Kraft, F., Waibel, A., 2006. Towards continuous speech recognition using surface electromyography. In: Proc. Interspeech, Pittsburgh, PA, pp. 573-576.
10. Jou, S.-C.S., Schultz, T., Waibel, A., 2007. Continuous electromyographic speech recognition with a multi-stream decoding architecture. In: Proc. IEEE Internat. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), Honolulu, Hawaii, pp. 401-404.
11. Kirchhoff, K., 1999. Robust Speech Recognition using Articulatory Information, Ph.D. Thesis. University of Bielefeld.
12. Leveau, B., Andersson, G., 1992. Output forms: data analysis and applications. In: Selected Topics in Surface Electromyography for Use in the Occupational Setting: Expert Perspective.
13. Maier-Hein, L., Metze, F., Schultz, T., Waibel, A., 2005. Session independent non-audible speech recognition using surface electromyography. In: IEEE Workshop on Automatic Speech Recognition and Understanding, San Juan, Puerto Rico, pp. 331-336.
14. Metze, F., 2005. Articulatory Features for Conversational Speech Recognition, Ph.D. Thesis. University of Karlsruhe.
15. Metze, F., Waibel, A., 2002. A flexible stream architecture for ASR using articulatory features. In: Proc. Internat. Conf. on Spoken Language Processing (ICSLP), Denver, Colorado, USA, pp. 2133-2136.
16. Morse, M.S., Day, S.H., Trull, B., Morse, H., 1989. Use of myoelectric signals to recognize speech. In: Proc. 11th Annual Conf. of the IEEE Engineering in Medicine and Biology Society, pp. 1793-1794.
17. Morse, M.S., Gopalan, Y.N., Wright, M., 1991. Speech recognition using myoelectric signals with neural networks. In: Proc. 13th Annual Conf. of the IEEE Engineering in Medicine and Biology Society, pp. 1877-1878.
18. Morse M.S., and O'Brien E.M. Research summary of a scheme to ascertain the availability of speech information in the myoelectric signals of neck and head muscles using surface electrodes. Comput. Biological Med. 16 6 (1986) 399-410
19. Schünke M., Schulte E., and Schumacher U. Prometheus - Lernatlas der Anatomie. Kopf und Neuroanatomie Vol. 3 (2006), Thieme Verlag, Stuttgart, New York
20. Sugie N., and Tsunoda K. A speech prosthesis employing a speech synthesizer - vowel discrimination from perioral muscle activities and vowel production. IEEE Trans. Biomed. Eng. 32 7 (1985) 485-490
21. Ueda N., Nakano R., Ghahramani Z., and Hinton G.E. Split and merge EM algorithm for improving gaussian mixture density estimates. J. VLSI Signal Process. 26 (2000) 133-140
22. Walliczek, M., Kraft, F., Jou, S.-C., Schultz, T., Waibel, A., 2006. Sub-word unit based non-audible speech recognition using surface electromyography. In: Proc. Interspeech, Pittsburgh, PA, pp. 1487-1490.
23. Wand, M., Schultz, T., in press. Speaker-adaptive speech recognition based on surface electromyography. In: BIOSTEC - BIOSIGNALS 2009 Best Papers. Communications in Computer and Information Science (CCIS) Series. Springer, Heidelberg.
24. Wand, M., Schultz, T., 2009b. Towards speaker-adaptive speech recognition based on surface electromyography. In: Proc. Biosignals, Porto, Portugal, pp. 155-162.
25. Wand, M., Toth, A., Jou, S.-C., Schultz, T., 2009. Impact of different speaking modes on EMG-based speech recognition. In: Proc. Interspeech, Brighton, United Kingdom.
26. Yu, H., Schultz, T., 2003. Enhanced tree clustering with single pronunciation dictionary for conversational speech recognition. In: Proc. Eurospeech, Geneva, Switzerland, pp. 1869-1872.
27. Yu, H., Waibel, A., 2000. Streamlining the front end of a speech recognizer. In: Proc. Internat. Conf. on Spoken Language Processing (ICSLP), Beijing, China, pp. 353-356.