Automatic detection of consonant omission in cleft palate speech

Abstract

Consonant omission is one of the most typical and common articulation disorders in cleft palate (CP) speech. The automatic evaluation of consonant omission provides an objective and aided diagnosis to speech-language pathologists and CP patients. This work proposes an automatic consonant omission method. The collection of pathologic speech data is far more difficult than that of normal speech. The speech samples applied in this work are collected from 80 CP patients, with annotation on the phonemic level by professional speech-language pathologists. The proposed method requires no pre-training or modeling, by taking advantages of priori knowledge of CP speech and Chinese phonetics. The classification between voiced initials and finals is a difficulty in Mandarin speech processing researches, this work proposes a time-domain waveform difference analysis method to address this difficulty. The proposed method achieves the accuracy of 88.9% for consonant omission detection in CP speech, and the sensitivity and specificity of the proposed method are 70.89% and 91.86% respectively.