关键词: 腭裂语音, 高鼻音, 线性预测, 稀疏表示, 倒谱

Abstract: The automatic hypernasality detection in cleft palate speech has important clinical value. The detected hypernasality grades provide critical information of assessment for velopharyngeal function. In this work, an automatic detection algorithm in cleft palate speech based on the vocal tract characteristics is proposed. Combining the high order and low order linear prediction cepstrum coefficient with the cepstrum coefficient, a feature set called LPCC-Cep is obtained. Using the proposed feature set as the acoustic characteristic parameter, a sparse representation classifier is applied to achieve the automatic four-grade hypernasality detection：normal, mild, moderate and severe hypernasality. The experimental results show that the proposed algorithm reaches high accuracy of  hypernasality detection. Among them, selecting LPCC-Cep feature set as the parameter, the accuracy of hypernasality detection is 83.38%.

Key words: cleft palate speech, hypernasality, linear prediction, sparse representation, cepstrum