%0 Journal Article %1 Parker2006 %A Parker, Mark %A Cunningham, Stuart %A Enderby, Pam %A Hawley, Mark %A Green, Phil %D 2006 %J Clin Linguist Phon %K Artificial Intelligence; Cerebral Palsy; Communication Aids for Disabled; Dysarthria; Humans; Multiple Sclero; Sound Spectrography; Speech Production Measurement; Therapy; Therapy, Computer-Assisted; Treatment Outcome; Vocabulary; sis %N 2-3 %P 149--156 %R 10.1080/02699200400026884 %T Automatic speech recognition and training for severely dysarthric users of assistive technology: the STARDUST project. %U http://dx.doi.org/10.1080/02699200400026884 %V 20 %X The STARDUST project developed robust computer speech recognizers for use by eight people with severe dysarthria and concomitant physical disability to access assistive technologies. Independent computer speech recognizers trained with normal speech are of limited functional use by those with severe dysarthria due to limited and inconsistent proximity to "normal" articulatory patterns. Severe dysarthric output may also be characterized by a small mass of distinguishable phonetic tokens making the acoustic differentiation of target words difficult. Speaker dependent computer speech recognition using Hidden Markov Models was achieved by the identification of robust phonetic elements within the individual speaker output patterns. A new system of speech training using computer generated visual and auditory feedback reduced the inconsistent production of key phonetic tokens over time.

@article{Parker2006, abstract = {The STARDUST project developed robust computer speech recognizers for use by eight people with severe dysarthria and concomitant physical disability to access assistive technologies. Independent computer speech recognizers trained with normal speech are of limited functional use by those with severe dysarthria due to limited and inconsistent proximity to "normal" articulatory patterns. Severe dysarthric output may also be characterized by a small mass of distinguishable phonetic tokens making the acoustic differentiation of target words difficult. Speaker dependent computer speech recognition using Hidden Markov Models was achieved by the identification of robust phonetic elements within the individual speaker output patterns. A new system of speech training using computer generated visual and auditory feedback reduced the inconsistent production of key phonetic tokens over time.}, added-at = {2014-07-19T20:59:22.000+0200}, author = {Parker, Mark and Cunningham, Stuart and Enderby, Pam and Hawley, Mark and Green, Phil}, biburl = {https://www.bibsonomy.org/bibtex/27d5797430393a262220c7bb7c9ab8018/ar0berts}, doi = {10.1080/02699200400026884}, groups = {public}, interhash = {9e398df8100ea3cb94af10737aab0828}, intrahash = {7d5797430393a262220c7bb7c9ab8018}, journal = {Clin Linguist Phon}, keywords = {Artificial Intelligence; Cerebral Palsy; Communication Aids for Disabled; Dysarthria; Humans; Multiple Sclero; Sound Spectrography; Speech Production Measurement; Therapy; Therapy, Computer-Assisted; Treatment Outcome; Vocabulary; sis}, number = {2-3}, pages = {149--156}, pii = {Q5787437868613MX}, pmid = {16428231}, timestamp = {2014-07-19T20:59:22.000+0200}, title = {Automatic speech recognition and training for severely dysarthric users of assistive technology: the STARDUST project.}, url = {http://dx.doi.org/10.1080/02699200400026884}, username = {ar0berts}, volume = 20, year = 2006 }