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针对严重构音障碍的辅助技术用户的自动语音识别与训练:星尘项目。

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

作者信息

Parker Mark, Cunningham Stuart, Enderby Pam, Hawley Mark, Green Phil

机构信息

Sheffield Speech and Language Therapy Agency, Sheffield, UK.

出版信息

Clin Linguist Phon. 2006 Apr-May;20(2-3):149-56. doi: 10.1080/02699200400026884.

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.

摘要

星尘项目开发了强大的计算机语音识别器,供八名患有严重构音障碍并伴有身体残疾的人使用,以接入辅助技术。因与“正常”发音模式的接近程度有限且不一致,那些使用经正常语音训练的独立计算机语音识别器的严重构音障碍患者,其功能用途有限。严重构音障碍的输出还可能表现为可区分的语音标记数量少,这使得目标单词的声学区分变得困难。通过识别个体说话者输出模式中的稳健语音元素,实现了使用隐马尔可夫模型的特定说话者计算机语音识别。一种使用计算机生成的视觉和听觉反馈的新语音训练系统,随着时间的推移减少了关键语音标记的不一致产生。

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