Automatic hypernasal detection in cleft palate speech based on formant parameters of mandarin
10.3760/cma.j.cn114453-20190716-00220
- VernacularTitle:基于汉语普通话共振峰参数的腭裂高鼻音自动识别研究
- Author:
Bochun MAO
1
;
Pingchuan MA
;
Chunli GUO
;
Ling HE
;
Hongxiang MEI
;
Heng YIN
Author Information
1. 口腔疾病研究国家重点实验室，国家口腔疾病临床医学研究中心，成都 610041（现在北京大学口腔医院正畸科 100081）
- Keywords:
Velopharyngeal insufficiency;
Voice;
Signal processing, computer-assisted;
Formant;
Acoustics
- From:
Chinese Journal of Plastic Surgery
2020;36(11):1246-1252
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To investigate the efficacy of 2 algorithm models, cascade channel model and combined wavelet with linear prediction coefficient(LPC), on extracting the hypernasal format parameters of cleft palate speech.Methods:The voice of 859 patients, 421 male and 438 female with average age of 12.1 years, were collected from the speech data of the Department of Cleft Lip and Palate Surgery of West China Hospital of Stomatology of Sichuan University. The patients were classified into 216 normal speech patients, 220 low-level hypernasal patients, 213 moderate-level hypernasal patients and 210 high-level hypernasal patients. 62 707 speech samples were collected. Cascade channel model and combined wavelet with LPC were used to combine the K-nearest neighbor classifier respectively to distinguish the hypernasal level, and the result were compared with the golden standard, i. e. the speech evaluation result. The result were analyzed statistically with chi-square test.Results:Compared to the cascaded channel model, levels combined wavelet with LPC achieved significantly higher accuracy of all hypernasal levels ( P<0.05). Among all different mis-classifications, the most common error of the 2 models was misjudging normal speech patients as low-level hypernasal patients (for cascaded channel model: 41/216, 18.98%; for combined wavelet with LPC: 32/216, 14.81%). Conclusions:Two algorithm models based on formant parameters for hypernasal recognition of cleft palate was established. Combined wavelet with LPC both realized the automatic identification of hypernasal level in Mandarin Chinese. The average classification accuracy of hypernasal level evaluation by using combined wavelet with LPC is higher.
