LANDMARK ANALYSIS OF DYSARTHRIC SPEECH

构音障碍患者的语音是高度可变的，甚至会导致受过训练的、依赖于说话者的语音识别器无法识别构音障碍语音。提出了一种从构音障碍语音的极小词汇量中识别单词的新方法。该方法使用基于地标的分类器(LMC)来识别语音中的声学事件序列。这种新方法不依赖于存储的语音数据库。对于较小的词集，每个标志性的系列将对应于一个唯一的单词或短语；从1到20和从20到100到10的数字构成了这样的集合。小词汇量对于远程电话和家电控制的语音接口等应用很有用。远程控制系统对喜欢开车的人很有用，并提高了独立性和安全性。这个项目将把正常受试者说出的数字的结果扩展到更多的单词和一些中度构音障碍。它还将确定这些标志性序列是否可以由LMC在构音障碍语音中自动检测。如果它们能被可靠地检测到，第二阶段将把分析扩展到更广泛的构音障碍语音和更大的词汇量，适合远程控制。拟议的商业应用：显然需要能够识别无序语音的语音识别技术。该软件可以用作AAC设备或环境控制器的免提接口。

The speech of individuals with dysarthria is highly variable and causes even trained, speaker-dependent speech recognizers to fail to recognize dysarthric speech. A novel method is proposed for recognizing words from a very small vocabulary of dysarthric speech. The method uses a "landmark"-based classifier (LMC) to identify sequences of acoustic events in speech. This new method does not depend on a stored database of speech. For small word sets, each landmark series will correspond to a unique word or phrase; the numbers from 1-20 and from 20-100 by tens form such a set. Small vocabularies are useful for applications such as speech interfaces for remote telephone and appliance control. Remote control systems are useful for motorically individuals and increase independence and safety. This project will extend the result for numbers spoken by normal subjects to more words and to some moderate dysarthrics. It will also determine whether these landmark series can be detected automatically in dysarthric speech by an LMC. If they can be detected robustly, Phase II will extend the analyses to a wider variety of dysarthric speech and a larger vocabulary, appropriate for remote control. PROPOSED COMMERCIAL APPLICATION: There is a clear need for speech recognition technology that will recognize disordered speech. The software could be used as a hands-free interface for AAC devices or environmental controllers.