腭裂语音高鼻音等级自动识别关键技术研究

项目名称： 腭裂语音高鼻音等级自动识别关键技术研究

项目编号： No.61503264

项目类型： 青年科学基金项目

立项/批准年度： 2016

项目学科： 其他

项目作者： 何凌

作者单位： 四川大学

项目金额： 19万元

中文摘要： 高鼻音是腭裂语音最常见的临床表现之一。对腭裂高鼻音的语音评估具有十分重要的临床意义，目前国际各唇腭裂治疗单位均通过专业语音师的主观判听实现，该方式受语音师经验及主观状态等影响因素较多。自动腭裂高鼻音等级识别将为腭裂语音评估提供客观辅助诊断，社会应用前景广泛。目前，国内外对腭裂语音信号处理研究仍属起步阶段，其最主要瓶颈为腭裂语音数据采集。课题针对该瓶颈，与国内最大最权威的腭裂语音治疗中心合作，建立样本广泛的权威腭裂语音数据库。目前，国内外对腭裂高鼻音语音研究停滞在对高鼻音有无的判别上，临床意义不大。临床实践中，医生与语音师更希望得到对高鼻音等级的识别。课题首次实现普通话腭裂语音高鼻音四个等级（正常、轻、中、重度）自动识别的关键问题研究：从发音机理探索腭裂高鼻音声学特征的形成；研究腭裂高鼻音等级敏感声学特征参数；探讨腭裂高鼻音声学特性与模式识别算法的结合。课题开展弥补了国内在该领域的研究空白。

中文关键词： 腭裂语音；高鼻音；病理语音；声学特征参数提取；分类器

英文摘要： Hypernasality is one the most typical characteristics of Cleft Palate (CP) speech. The grades of hypernasality reflect different open port sizes of velopharyngeal. It is an important indicator to evaluate operation effectiveness. Moreover, ti provides assistant diagnosis for the necessity of carrying out the follow-up operation and treatment. Currently, the evaluation of CP speech is mostly carried out by experienced speech therapists. However, it strongly depends on their clinical experience and subjective judgment. Automatic evaluation of CP speech provides an objective and assistant assessment for both CP patients and doctors. It has various clinical applications. Currently, the major bottleneck in the field of CP speech research is the collection of CP speech data, which is tremendously limited by the number of patients, the accent of speakers, and thoughtful design of vocabulary list, which highly reflects typical CP speech characteristics. The majority of current CP speech researches are based on small size of CP speech databases, which only include several vowels or words. In this work, a more extensive CP database is applied. The speech data are collected by the Department of Cleft Lip and Palate (DCLP), Hospital of Stomatology, Sichuan University, which has the largest number of CLP patients in China. The majority of current researches on CP speech hypernasality analysis detect the existence of hypernasality only. However, CP speech therapists are more interested in finding out different levels of hypernasality, which indicates the velopharyngeal gap size. Whereas, very few works have been done in this field. In this work, we propose an automatic CP speech hypernasality grades evaluation algorithm. We investigate the acoustic features extraction methods and pattern recognition algorithms to classify four hypernasality grades: normal, mild,moderate and severe. This is the first domestic research on this topic.

英文关键词： cleft palate speech;hypernasality;pathology voice;acoustic feature extraction;classifier