2006 Fiscal Year Final Research Report Summary
Research on the development and the make of the assistive technology tools to promote communication ability for person with intellectual disabilities
| Project/Area Number |
16200048
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| Research Category |
Grant-in-Aid for Scientific Research (A)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Educational technology
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| Research Institution | Kobe University |
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Principal Investigator |
NAKABAYASHI Toshitaka Kobe University, Faculty of Human Development, Professor, 発達科学部, 教授 (50164265)
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| Co-Investigator(Kenkyū-buntansha) |
KISHIMOTO Hajime Kobe University, Faculty of Human Development, Professor, 発達科学部, 教授 (80030592)
TAKAHASHI Tadashi Kobe University, Faculty of Human Development, Professor, 発達科学部, 教授 (30179494)
INAGAKI Shigenori Kobe University, Faculty of Human Development, Professor, 発達科学部, 教授 (70176387)
TATUMI Takeo Tokyonoukou University, Information media center, Associate Professor, 総合情報メディアセンター, 助教授 (70257195)
HARADA Yasunari Waseda University, Faculty of Low, Professor, 法学部, 教授 (80189711)
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| Project Period (FY) |
2004 – 2006
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| Keywords | Intellectual Disability / VOCA / Communication Assistive tool / Speech Recognition |
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| Research Abstract |
1. Autistic disorder is a neurobiological disorder of development for one's lifetime. Children with autistic disorder have difficulty communicating and interacting with others. We developed and tested a suit of five PDA application software "Picture Aid" for Children with intellectual disability (include autistic disorder) need extensive or pervasive supports. (1) aid for ordinal procedure, (2) timer,. (3) picture card with voice, (4)words slot machine game with picture, and (5) puzzle. We designed our aid for ordinal procedure with many customizable pictures. Customization is done on a PC. After customization, user transmit new application software to PDA.
2. Recently, the accuracy of speaker-independent speech recognition has been remarkably improved by use of stochastic modeling of speech. However, the use of those acoustic models causes degradation of speech recognition for a person with different speech style (e.g., articulation disorders). We had tried to build the acoustic model for a person with articulation disorders (a person with articulation disorders by athetoid type of cerebral palsy). The articulation of the first utterance tended to become unstable due to strain of a muscle and that caused degradation of speech recognition, where MFCC (Mel Frequency Cepstral Coefficients) iwas used as speech features. Therefore we proposed a robust feature extraction method based on PCA (Principal Component Analysis) instead of MFCC. Its effectiveness was confirmed by word recognition experiments.
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Research Products
(8 results)
