关键词: 腭裂语音, 高鼻音, 听觉模型, 同步检测器

Abstract: The automatic detection of hypernasality degrees in cleft palate speech can provide effective, objective and non-invasive basis for the assessment of velopharyngeal function in clinical. In this work, an automatic detection system of hypernasality degrees in cleft palate has been researched. The human auditory model is applied to extract the inner presentation of speech signal as the front-end processing, and the SLR（Soft-Limited Ratio） spectral features extracted from the synchronous detector is used as the acoustic characteristic parameters. The 1-v-1 SVM （1-v-1 Support Vector Machine） is utilized to automatically detect the hypernasality degrees （normal, mild, moderate and severe hypernasality）. Experimental data include total 3 086 speeches from 56 kids, the comparisons of filter bank’s kind and number, synchronous detector and lateral inhibitory network are discussed. And the results show that the Gammatone filter based on ERB （Equivalent Rectangular Bandwidth） scale performs better than the wavelet-packet filter based on Bark scale, and the filter bank with 54 channels can effectively weigh the time cost and recognition accuracy of our algorithm, and SLR spectral features extracted from the synchronous detector has better recognition than LIN spectral features extracted from the lateral inhibition network. The highest accuracy of the automatic detection of four-hypernasality degree is 91.50%.

Key words: cleft palate speech, hypernasality, auditory model, synchronous detector