References
- 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.
- Blaney, B., & Wilson, J. (2000). Acoustic variability in dysarthria and computer speech recognition. Clinical Linguistics & Phonetics, 14(4), 307–327.
- 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.
- Caves, K., Boemler, S., & Cope, B. (2007). Development of an automatic recognizer for dysarthric speech. Proceedings of the RESNA Annual Conference, Phoenix, AZ.
- Chen, F., & Kostov, A. (1997). Optimization of dysarthric speech recognition. Proceedings of the IEEE Engineering in Medicine and Biology Society Conference, Chicago, 1436–1439.
- 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.
- Darley, F. L., Aronson, A. E., & Brown, J. R. (1969). Differential diagnostic patterns of dysarthria. Journal of Speech and Hearing Research, 12, 246–269.
- 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.
- 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.
- 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.
- 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.
- 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.
- Hanson, E., Yorkston, K., & Beukelman, D. (2004). Speech supplementation techniques for dysarthria: A systematic review. Journal of Medical Speech-Language Pathology, 12, 9–29.
- 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.
- Hawley, M. S. (2002). Speech recognition as an input to electronic assistive technology. British Journal of Occupational Therapy, 65, 15–20.
- 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.
- 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.
- 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.
- Higginbotham, D. J. (1992). Evaluation of keystroke savings across five assistive communication technologies. Augmentative and Alternative Communication, 8, 258–272.
- Hosom, J. P. (2009). Speaker-independent phoneme alignment using transition-dependent states. Speech Communication, 51(4), 352–368.
- 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.
- 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.
- 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.
- Hustad, K. C., Jones, T., & Dailey, S. (2003). Implementing speech supplementation strategies. Journal of Speech, Language, and Hearing Research, 46, 462–474.
- 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.
- 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.
- Koester, H., & Levine, S. (1998). Model simulation of user performance with word prediction. Augmentative and Alternative Communication, 14, 25–35.
- 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.
- 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.
- Magnuson, T., & Blomberg, M. (2000). Acoustic analysis of dysarthric speech and some implications for automatic speech recognition. TMH-QPSR, 41, 19–30.
- Magnuson, T., & Hunnicutt, S. (2002). Measuring the effectiveness of word prediction: The advantage of long-term use. TMH-QPSR, 43, 57–67.
- 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.
- 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
- 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.
- Polur, P. D., & Miller, G. E. (2005). Effect of high-frequency spectral components in computer recognition of dysarthric speech based on a Mel-cepstral stochastic model. Journal of Rehabilitation Research and Development, 42, 363–371.
- 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.
- 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.
- Rosen, K., & Yampolsky, S. (2000). Automatic speech recognition and a review of its functioning with dysarthric speech. Augmentative and Alternative Communication, 16, 48–60.
- Rosengren, E. (2000). Perceptual analysis of dysarthric speech in the ENABL project. TMH-QPSR, 41, 13–18.
- 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?q=cache:fS fdtom_TMJ:scholar.google.com/+Sawhney+and+Wheeler+1999&hl=en
- Talbot, N. (2000). Improving the speech recognition in the ENABL project. TMH-QPSR, 41, 31–38.
- 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?doi=10.1.1.72.4944&rep=rep1&type=pdf
- Venkatagiri, H. (1994). Effect of window size on rate of communication in a lexical prediction AAC system. Augmentative and Alternative Communication, 10, 105–112.
- Venkatagiri, H. S. (2002). Speech recognition technology applications in communication disorders. American Journal of Speech-Language Pathology, 11, 323–332.
- 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.
- Yorkston, K., Beukelman, D., Hakel, M., & Dorsey, M. (2007). Sentence Intelligibility Test [Computer software]. Lincoln, NE: Madonna Rehabilitation Hospital.