Hidden Markov model classification of myoelectric signals in speech

IEEE Engineering in Medicine and Biology Magazine

Volumn 21, Issue 5, 2002, Pages 143-146

  Chan, A D C ^a,b   Englehart, K ^a,b   Hudgins, B ^a   Lovely, D F ^b  

^a UNIVERSITY OF NEW BRUNSWICK   (Canada)

^b UNIVERSITY OF NEW BRUNSWICK   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ACOUSTICS; ARTICLE; CLASSIFICATION; MODEL; MYOELECTRICITY; NOISE; POSITIVE END EXPIRATORY PRESSURE; SPEECH;

ELECTROMYOGRAPHY; FACIAL MUSCLES; HUMANS; MARKOV CHAINS; MODELS, BIOLOGICAL; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SPEECH; SPEECH PRODUCTION MEASUREMENT;

EID: 0036750225     PISSN: 07395175     EISSN: None     Source Type: Journal    
DOI: 10.1109/MEMB.2002.1044184     Document Type: Article

References (6)

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3. S.E. Bou-Ghazale and J.H.L. Hansen, "A comparative study of traditional and newly proposed features for recognition of speech under stress," IEEE Trans. Speech Audio Processing, vol. 8, no. 4., pp. 429-442, 2000.
4. A.D.C. Chan, K. Englehart, B. Hudgins, and D.F. Lovely, "Myoelectric signals to augment speech recognition," Med. Biol. Eng. Comput., to be published.
5. M. Zardoshti, B.C. Wheeler, K. Badie, and R. Hashemi, "Evaluation of EMG features for movement control of prostheses," in Proc. 15th Annu. Int. Conf. IEEE EMBS, 1993. vol. 15, no. 3, pp. 1141-1142.
6. L. Rabiner and B.H. Juang, Fundamentals of Speech Recognition. Englewood Cliffs, NJ: Prentice-Hall, 1993.