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Speech Clarity Index (Ψ): A Distance-based Speech Quality Indicator And Recognition Rate Prediction For Dysarthric Speakers With Cerebral Palsy

机译:语音清晰度指数(Ψ):基于距离的语音麻痹性说话者说话者的语音质量指标和识别率预测

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摘要

It is a tedious and subjective task to measure severity of a dysarthria by manually evaluating his/her speech using available standard assessment methods based on human perception. This paper presents an automated approach to assess speech quality of a dysarthric speaker with cerebral palsy. With the consideration of two complementary factors, speech consistency and speech distinction, a speech quality indicator called speech clarity index (Ψ) is proposed as a measure of the speaker's ability to produce consistent speech signal for a certain word and distinguished speech signal for different words. As an application, it can be used to assess speech quality and forecast speech recognition rate of speech made by an individual dysarthric speaker before actual exhaustive implementation of an automatic speech recognition system for the speaker. The effectiveness of Ψ as a speech recognition rate predictor is evaluated by rank-order inconsistency, correlation coefficient, and root-mean-square of difference. The evaluations had been done by comparing its predicted recognition rates with ones predicted by the standard methods called the articulatory and intelligibility tests based on the two recognition systems (HMM and ANN). The results show that Ψ is a promising indicator for predicting recognition rate of dysarthric speech. All experiments had been done on speech corpus composed of speech data from eight normal speakers and eight dysarthric speakers.
机译:通过使用基于人类感知的可用标准评估方法手动评估语音异常来测量构音障碍的严重程度是一项繁琐而主观的任务。本文提出了一种自动方法来评估患有脑瘫的发音异常的说话者的语音质量。考虑到语音互补性和语音区分性这两个互补因素,提出了一种语音质量指标,称为语音清晰度指数(a),用于衡量说话者针对某个单词产生一致的语音信号以及针对不同单词产生区分的语音信号的能力。 。作为一种应用程序,它可以用于评估语音质量,并预测各个发音异常的说话者的语音识别率,然后再实际穷举实施自动语音识别系统。 rank作为语音识别率预测指标的有效性通过等级顺序不一致,相关系数和差异的均方根来评估。评估是通过将其预测的识别率与基于两种识别系统(HMM和ANN)的称为清晰度和清晰度测试的标准方法所预测的识别率进行比较而完成的。结果表明,Ψ是预测构音障碍语音识别率的有前途的指标。所有实验都是在语音语料库上进行的,该语料库由来自八名正常说话者和八位发音异常者的语音数据组成。

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