Evaluation of a speech recognition prototype for speakers with moderate and severe dysarthria: A preliminary report

AAC: Augmentative and Alternative Communication

Volumn 26, Issue 4, 2010, Pages 267-277

  Fager, Susan K ^a   Beukelman, David R ^b   Jakobs, Tom ^c   Hosom, John Paul ^d  

^a MADONNA REHABILITATION HOSPITAL   (United States)

^b UNIVERSITY OF NEBRASKA LINCOLN   (United States)

^c Invotek Inc   (United States)

^d OREGON HEALTH AND SCIENCE UNIVERSITY   (United States)

Author keywords

Dysarthria; Speech recognition; Supplemented speech; Voice recognition

Indexed keywords

AUTOMATIC SPEECH RECOGNITION; DYSARTHRIA; DYSARTHRIC SPEECH; SENTENCE INTELLIGIBILITY; STATISTICAL SIGNIFICANCE; SUPPLEMENTED SPEECH; VOICE RECOGNITION; WORD PREDICTION;

SPEECH RECOGNITION;

ADULT; ARTICLE; AUTOMATIC SPEECH RECOGNITION; COMMUNICATION AID; COMPARATIVE STUDY; DYSARTHRIA; EVALUATION; FEMALE; HOSPITALIZATION; HUMAN; MALE; MIDDLE AGED; PATHOPHYSIOLOGY; SPEECH INTELLIGIBILITY; STANDARD;

ADULT; COMMUNICATION AIDS FOR DISABLED; DYSARTHRIA; FEMALE; HUMANS; MALE; MIDDLE AGED; SEVERITY OF ILLNESS INDEX; SPEECH INTELLIGIBILITY; SPEECH RECOGNITION SOFTWARE; YOUNG ADULT;

EID: 78649347622     PISSN: 07434618     EISSN: 14773848     Source Type: Journal    
DOI: 10.3109/07434618.2010.532508     Document Type: Article

References (46)

1. Beukelman, D. R., Garrett, K. L., & Yorkston, K. M. (2007). Augmentative communication strategies for adults with acute or chronic medical conditions. Baltimore: Brookes Publishing Co.
2. Blaney, B., & Wilson, J. (2000). Acoustic variability in dysarthria and computer speech recognition. Clinical Linguistics & Phonetics, 14(4), 307-327.
3. Cannito, M., Suiter, D., Chorna, L., Beverly, D., Wolf, T., & Pfeiffer, R. (2008). Speech intelligibility in idiopathic Parkinson's disease before and after amplitude therapy. American Speech-Language Hearing Association, 13, 105.
4. Caves, K., Boemler, S., & Cope, B. (2007). Development of an automatic recognizer for dysarthric speech. Proceedings of the RESNA Annual Conference, Phoenix, AZ.
5. Chen, F., & Kostov, A. (1997). Optimization of dysarthric speech recognition. Proceedings of the IEEE Engineering in Medicine and Biology Society Conference, Chicago, 1436-1439.
6. Coleman, C. L., & Meyers, L. S. (1991). Computer recognition of the speech of adults with cerebral palsy and dysarthria. Augmentative and Alternative Communication, 7, 34-42.
7. Darley, F. L., Aronson, A. E., & Brown, J. R. (1969). Differential diagnostic patterns of dysarthria. Journal of Speech and Hearing Research, 12, 246-269.
8. Ferrier, L. J., Jarrell, N., Carpenter, T., & Shane, H. C. (1992). A case study of a dysarthric speaker using the DragonDictate voice recognition system. Journal for Computer Users in Speech and Hearing, 8, 33-53.
9. Ferrier, L. J., Shane, H. C., Ballard, H. F., Carpenter, T., & Benoit, A. (1995). Dysarthric speakers' intelligibility and speech characteristics in relation to computer speech recognition. Augmentative and Alternative Communication, 11, 165-174.
10. Fried-Oken, M. (1985). Voice recognition device as a computer interface for motor and speech impaired people. Archives of Physical Medicine and Rehabilitation, 66, 678-681.
11. Green, P., Carmichael, J., Hatzis, A., Enderby, P., Hawley, M., & Parker, M. (2003). Automatic speech recognition with sparse training data for dysarthric speakers. Proceedings of the 8th European Conference on Speech Communication and Technology (Eurospeech'03), Geneva, Switzerland, 1189-1192.
12. Hanson, E. K., Beukelman, D. R., Heidemann, J. K., & Shutts-Johnson, E. (2009). The impact of alphabet supplementation and word prediction on sentence intelligibility of electronically distorted speech. Speech Communication, 52, 99-105.
13. Hanson, E., Yorkston, K., & Beukelman, D. (2004). Speech supplementation techniques for dysarthria: A systematic review. Journal of Medical Speech-Language Pathology, 12, 9-29.
14. Hatzis, A., Green, P., Carmichael, J., Cunningham, S., Palmer, R., Parker, M., & O'Neill, P. (2003). An integrated toolkit deploying speech technology for computer based speech training with application to dysarthric speakers. Proceedings of the 8th European Conference on Speech Communication and Technology (Eurospeech'03), Geneva, Switzerland, 2213-2216.
15. Hawley, M.S. (2002). Speech recognition as an input to electronic assistive technology. British Journal of Occupational Therapy, 65, 15-20.
16. Hawley, M.S., Enderby, P., Green, P., Brownsell, S., Hatzis, A., Parker, M., Palmer, R. (2003). STARDUST: Speech training and recognition for dysarthric users of assistive technology. In G.M. Craddock et al. (Eds.), Assistive technology-Shaping the future (pp. 959-964). Amsterdam: IOS Press.
17. Hawley, M., Enderby, P., Green, P., Cunningham, S., Brownsell, S., Carmichael, J.,.. Palmer, R. (2007). A speech-controlled environmental control system for people with severe dysarthria. Medical Engineering & Physics, 29 (5), 586-593.
18. Hawley, M. S., Enderby, P., Green, P., Cunningham, S., & Palmer, R. (2006). Development of a voice-input voice-output communication aid (VIVOCA) for people with severe dysarthria. Lecture Notes in Computer Science, 4061, 882-885.
19. Higginbotham, D. J. (1992). Evaluation of keystroke savings across five assistive communication technologies. Augmentative and Alternative Communication, 8, 258-272.
20. Hosom, J.P. (2009). Speaker-independent phoneme alignment using transition-dependent states. Speech Communication, 51(4), 352-368.
21. Hosom, J.P., Cole, R.A., & Cosi, P. (1998). Improvements in neural-network training and search techniques for continuous digit recognition. Australian Journal of Intelligent Information Processing Systems, 5(4), 277-284.
22. Hustad, K. C., Auker, J., Natale, N., & Carlson, R. (2003). Improving intelligibility of speakers with profound dysarthria and cerebral palsy. Augmentative and Alternative Communication, 19, 187-198.
23. Hustad, K. C., & Beukelman, D. R. (2001). Effects of linguistic cues and stimulus cohesion on intelligibility of severely dysarthric speech. Journal of Speech, Language, and Hearing Research, 44, 497-510.
24. Hustad, K. C., Jones, T., & Dailey, S. (2003). Implementing speech supplementation strategies. Journal of Speech, Language, and Hearing Research, 46, 462-474.
25. Hux, K., Rankin-Erickson, J. L., Manasse, N. J., & Lauritzen, E. (2000). Accuracy of three speech recognition systems: Case study of dysarthric speech. Augmentative and Alternative Communication, 16, 186-196.
26. Judge, S., Robertson, Z., Hawley, M., & Enderby, P. (2009). Speech-driven environmental control systems-a qualitative analysis of users' perceptions. Disability & Rehabilitation: Assistive Technology, 4, 151-157.
27. Koester, H., & Levine, S. (1998). Model simulation of user performance with word prediction. Augmentative and Alternative Communication, 14, 25-35.
28. Kotler, A., & Thomas-Stonell, N. (1997). Effects of speech training on the accuracy of speech recognition for an individual with a speech impairment. Augmentative and Alternative Communication, 13, 71-80.
29. Lesher, G. W., Moulton, B. J., & Higginbotham, J. (1999). Effect of ngram order and training text size on word prediction. Proceedings of the RESNA Annual Conference, Arlington, VA.
30. Magnuson, T., & Blomberg, M. (2000). Acoustic analysis of dysarthric speech and some implications for automatic speech recognition. TMH-QPSR, 41, 19-30.
31. Magnuson, T., & Hunnicutt, S. (2002). Measuring the effectiveness of word prediction: The advantage of long-term use. TMH-QPSR, 43, 57-67.
32. Manasse, N., Hux, K., & Rankin-Erickson, J. (2000). Speech recognition training for enhancing written language generation by a traumatic brain injury survivor. Brain Injury, 14 (11), 1015-1034.
33. Omar, S., Morales, C., & Cox, S.J. (2009). Modeling errors in automatic speech recognition for dysarthric speakers. EURASIP Journal on Advances in Signal Processing. Retrieved August 31, 2009, from http://www.hindawi. com/journals/asp/2009/308340html
34. Parker, M., Cunningham, S., Enderby, P., Hawley, M., & Green, P. (2006). Automatic speech recognition and training for severely dysarthric users of assistive technology: The STARDUST project. Clinical Linguistics and Phonetics, 20, 149-156.
35. Polur, P. D., & Miller, G. E. (2005). Effect of high-frequency spectral components in computer recognition of dysarth-ric speech based on a Mel-cepstral stochastic model. Journal of Rehabilitation Research and Development, 42, 363-371.
36. Patel, R., & Roy, D. (1998). Teachable interfaces for individuals with dysarthric speech and severe physical disabilities. In Proceedings of the AAAI Workshop on Integrating Artificial Intelligence and Assistive Technology (pp. 40-47). Madison, WI: AAAI Press.
37. Raghavendra, P., Rosengren, E., & Hunnicutt, S. (2001). An investigation of different degrees of dysarthric speech as input to speaker-adaptive and speaker-dependent recognition systems. Augmentative and Alternative Communication, 17, 265-275.
38. Rosen, K., & Yampolsky, S. (2000). Automatic speech recognition and a review of its functioning with dysarthric speech. Augmentative and Alternative Communication, 16, 48-60.
39. Rosengren, E. (2000). Perceptual analysis of dysarthric speech in the ENABL project. TMH-QPSR, 41, 13-18.
40. Sawhney, N., & Wheeler, S. (1999). Using phonological context for improved recognition of dysarthric speech. Retrieved October 13, 2009, from http://74.125.155.132/scholar?qcache:fSFdtom-TMJ:scholar.google.com/\+ Sawhney\+and\+Wheeler\+1999&hlen
41. Talbot, N. (2000). Improving the speech recognition in the ENABL project. TMH-QPSR, 41, 31-38.
42. Trnka, K., Yarrington, D., McCoy, K., & Pennington, C. (2005). The keystroke savings limit in word prediction for AAC. Retrieved March 11, 2009, from http://citeseerx. ist.psu.edu/viewdoc/download?doi10.1.1.72.4944&rep rep1&typepdf
43. Venkatagiri, H. (1994). Effect of window size on rate of communication in a lexical prediction AAC system. Augmentative and Alternative Communication, 10, 105-112.
44. Venkatagiri, H. S. (2002). Speech recognition technology applications in communication disorders. American Journal of Speech-Language Pathology, 11, 323-332.
45. Wan, V., & Carmichael, J. (2005). Polynomial dynamic time warping kernel support vector machines for dysarthric speech recognition with sparse training data. Proceedings INTERSPEECH, 3321-3324.
46. Yorkston, K., Beukelman, D., Hakel, M., & Dorsey, M. (2007). Sentence Intelligibility Test [Computer software]. Lincoln, NE: Madonna Rehabilitation Hospital.