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Items: 1 to 20 of 276

1.

A speech-controlled environmental control system for people with severe dysarthria.

Hawley MS, Enderby P, Green P, Cunningham S, Brownsell S, Carmichael J, Parker M, Hatzis A, O'Neill P, Palmer R.

Med Eng Phys. 2007 Jun;29(5):586-93. Epub 2006 Oct 17.

PMID:
17049905
2.
3.

Automatic speech recognition and training for severely dysarthric users of assistive technology: the STARDUST project.

Parker M, Cunningham S, Enderby P, Hawley M, Green P.

Clin Linguist Phon. 2006 Apr-May;20(2-3):149-56.

PMID:
16428231
5.
6.

Estimation of phoneme-specific HMM topologies for the automatic recognition of dysarthric speech.

Caballero-Morales SO.

Comput Math Methods Med. 2013;2013:297860. doi: 10.1155/2013/297860. Epub 2013 Oct 8.

7.
8.

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

Fager SK, Beukelman DR, Jakobs T, Hosom JP.

Augment Altern Commun. 2010 Dec;26(4):267-77. doi: 10.3109/07434618.2010.532508.

PMID:
21091303
9.

Evaluation of tooth-click triggering and speech recognition in assistive technology for computer access.

Simpson T, Gauthier M, Prochazka A.

Neurorehabil Neural Repair. 2010 Feb;24(2):188-94. doi: 10.1177/1545968309341647. Epub 2009 Aug 13.

PMID:
19679651
10.

Multiexpert automatic speech recognition using acoustic and myoelectric signals.

Chan AD, Englehart KB, Hudgins B, Lovely DF.

IEEE Trans Biomed Eng. 2006 Apr;53(4):676-85.

PMID:
16602574
11.

Fuzzy support vector machines for adaptive Morse code recognition.

Yang CH, Jin LC, Chuang LY.

Med Eng Phys. 2006 Nov;28(9):925-31. Epub 2006 Jun 27.

PMID:
16807054
12.

Counter-propagation network with variable degree variable step size LMS for single switch typing recognition.

Yang CH, Luo CH, Yang CH, Chuang LY.

Biomed Mater Eng. 2004;14(1):23-32.

PMID:
14757950
13.

Dysarthric speech: a comparison of computerized speech recognition and listener intelligibility.

Doyle PC, Leeper HA, Kotler AL, Thomas-Stonell N, O'Neill C, Dylke MC, Rolls K.

J Rehabil Res Dev. 1997 Jul;34(3):309-16.

PMID:
9239624
14.

Telephony-based voice pathology assessment using automated speech analysis.

Moran RJ, Reilly RB, de Chazal P, Lacy PD.

IEEE Trans Biomed Eng. 2006 Mar;53(3):468-77.

PMID:
16532773
15.

A multi-views multi-learners approach towards dysarthric speech recognition using multi-nets artificial neural networks.

Shahamiri SR, Salim SS.

IEEE Trans Neural Syst Rehabil Eng. 2014 Sep;22(5):1053-63. doi: 10.1109/TNSRE.2014.2309336. Epub 2014 Mar 11.

PMID:
24760940
16.

Voice recognition device as a computer interface for motor and speech impaired people.

Fried-Oken M.

Arch Phys Med Rehabil. 1985 Oct;66(10):678-81.

PMID:
2932084
17.
18.

A voice-input voice-output communication aid for people with severe speech impairment.

Hawley MS, Cunningham SP, Green PD, Enderby P, Palmer R, Sehgal S, O'Neill P.

IEEE Trans Neural Syst Rehabil Eng. 2013 Jan;21(1):23-31. doi: 10.1109/TNSRE.2012.2209678. Epub 2012 Aug 3.

PMID:
22875259
19.

State of the art in continuous speech recognition.

Makhoul J, Schwartz R.

Proc Natl Acad Sci U S A. 1995 Oct 24;92(22):9956-63.

20.

An AAC application using speaking partner speech recognition to automatically produce contextually relevant utterances: objective results.

Wisenburn B, Higginbotham DJ.

Augment Altern Commun. 2008;24(2):100-9. doi: 10.1080/07434610701740448.

PMID:
18465364

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