A visual speech training aid for persons with hearing impairments has been developed using a Windows-based multimedia computer. In previous papers, the signal processing steps and display options have been described for giving real-time feedback about the quality of pronunciation for 10 steady-state American English monopthong vowels (/aa/, /iy/, /uw/, /ae/, /er/, /ih/, /eh/, /ao/, /ah/, and /uh/). This vowel training aid is thus referred to as a Vowel Articulation Training Aid (VATA). In the present paper, methods are described to develop a monophone-based Hidden Markov Model/Neural Network recognizer such that real time visual feedback can be given about the quality of pronunciation of short words and phrases. Experimental results are reported which indicate a high degree of accuracy for labeling and segmenting the CVC database developed for "training" the display.
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机译：使用基于Windows的多媒体计算机开发了具有听力障碍的人的视觉语音培训援助。在先前的论文中，已经描述了信号处理步骤和显示选项，用于提供关于10个稳态英语Monopthong元音的发音质量的实时反馈（/ aa /，/，/ iy /，/ uw /，/ ae / ，/ er /，/ ih /，/ eh /，/ ao /，/ ah /，和/ uh /）。因此，这种元音训练辅助辅助辅助作为元音铰接培训援助（VATA）。在本文中，描述了制定基于单声道的隐马尔可夫模型/神经网络识别器的方法，使得可以对短语和短语的发音质量提供实时视觉反馈。报告了实验结果表明标记和分割开发的“培训”显示器的CVC数据库的高精度。
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