Investigation of an HMM/ANN hybrid structure in pattern recognition application using cepstral analysis of dysarthric (distorted) speech signals

References 

1. Menéndez-Pidal X, Polikoff JB, Peters SM, Leonzio JE, Bunnell HT, et al. The Nemours database of Dysarthric speech. In: Proceedings of the fourth international conference on spoken language processing, October 3–6, 1996, Philadelphia, PA, USA.
2. Gold CJ. Cerebral palsy-John Coopersmith Gold. Berkeley Heights, NJ: Enslow publishers, Inc.; 2001;
3. Noyes JM, Frankish CR. Speech recognition technology for individuals with disabilities. Augmentative Alternative Commun. 1992;8:297–303
4. Sy BK, Horowitz DM. A statistical causal model for the assessment of Dysarthric speech and the utility of computer based speech recognition. IEEE Trans Biomed Eng. 1993;40(12):1282–1298
   • MEDLINE
   • CrossRef
5. Patel R. Identifying information bearing prosodic parameters in severely dysarthric speech. Doctoral dissertation, University of Toronto; 2000.
6. Goodenough C, Rosen M. Towards a method for computer interface design using speech recognition. In: Proceedings of the 14th annual RESNA conference. Kansas City, 1991. RESNA Press; 1991;p. 328–329
7. Rabiner LR. A tutorial on hidden Markov models and selected applications in speech recognition. Proc. IEEE. 1989;77(2):257–286
8. Jelinek F. Statistical methods for speech recognition (Language, speech and communication: a Bradford book). Bradford book. The MIT Press, Cambridge, MA, January 1998.
9. Kavcic A, Jose Moura MF. The Viterbi algorithm and Markovian noise memory. IEEE Trans Information Theory. 2000;46(1):291–301
10. Deller JR, Hsu D, Ferrier LJ. On the use of hidden Markov modeling for recognition of Dysarthric speech. Computer Methods Programs Biomed. 1991;35:125–139
11. Simon H. Neural networks—a comprehensive foundation. 2nd ed.. Prentice-Hall, Inc.; 1999;p. 1–34, 117–246, 635–58
12. Jayaram G, Abdelhamied K. Experiments in dysarthric speech recognition using artificial neural networks. J Rehabil Res Dev. 1995;32(2):162–169
    • MEDLINE
13. Hassanein KS. A neural predictive HMM architecture for speech and speaker recognition. Doctoral dissertation, University of Waterloo; 1994.
14. Hosom JP. Automatic time alignment of phonemes using acoustic-phonetic information. Doctoral dissertation, Oregon Graduate Institute of Science and Technology; 2000.
15. Davis SB, Mermelstein P. Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences. IEEE Trans Acoust Speech Signal Processing. 1980;28(4):357–366
16. Mak B, Tam YC, Li Q. Discriminative auditory-based features for robust speech recognition. IEEE Trans Speech Audio Processing. 2004;12(1):27–36
17. Polur PD, Miller GE. Effect of high-frequency spectral components in computer recognition of dysarthric speech based on a Mel-cepstral stochastic model. J Rehabil Res Dev. 2005;42(3):363–372
    • MEDLINE
    • CrossRef