Speech confusion index . /: A confusion-based speech quality indicator and recognition rate prediction for dysarthria

This paper presents an automated method to help us assess the speech quality of a dysarthric speaker, in place of laborious and subjective manual methods. The assessment result can be used as a good indicator for predicting the accuracy of speech recognition. The so-called speech confusion index . / is proposed to measure the speech disorder severity of a speaker in terms of how easily his/her speech signal may be misrecognized to other unintended words. Based on signal processing without any high-level information, the dynamic-time-warping technique incorporated with adaptive slope constraint and ac- cumulative mismatch score is used to measure a distance between any two speech signals of a same word or two different words. Compared to the articulatory and intelligibility tests, the proposed indicator was shown to have more predictability on the recognition rates ob- tained from the Hidden Markov Model (HMM) and Artificial Neural Networks (ANN). Based on three evaluation criteria, namely root-mean-square difference, correlation coefficient and rank-order inconsistency, the experimental results on a phoneme-balance set showed that achieved better prediction than both articulatory and intelligibility tests. Another experiment on a reduced training set is made to investigate the robustness of the proposed indicator. Finally, a detailed analysis of speech confusion is done at the phoneme level.