基于元音空间面积的超鼻音检测。

Detection of hypernasality based on vowel space area.

机构信息

Department of Electronics and Electrical Engineering, Indian Institute of Technology Guwahati, Guwahati 781039, India.

Department of Electrical and Electronics Engineering, Visvesvaraya National Institute of Technology (VNIT), Nagpur, Maharashtra 440010, India

出版信息

J Acoust Soc Am. 2018 May;143(5):EL412. doi: 10.1121/1.5039718.

DOI:10.1121/1.5039718

PMID:29857767

Abstract

This study proposes a method for differentiating hypernasal-speech from normal speech using the vowel space area (VSA). Hypernasality introduces extra formant and anti-formant pairs in vowel spectrum, which results in shifting of formants. This shifting affects the size of the VSA. The results show that VSA is reduced in hypernasal-speech compared to normal speech. The VSA feature plus Mel-frequency cepstral coefficient feature for support vector machine based hypernasality detection leads to an accuracy of 86.89% for sustained vowels and 89.47%, 90.57%, and 91.70% for vowels in contexts of high pressure consonants /k/, /p/, and /t/, respectively.

摘要

本研究提出了一种使用元音空间区域（VSA）区分超鼻音语音和正常语音的方法。超鼻音在元音频谱中引入额外的共振峰和反共振峰对，导致共振峰的移动。这种移动会影响 VSA 的大小。结果表明，与正常语音相比，超鼻音语音中的 VSA 减小。基于 VSA 特征和梅尔频率倒谱系数特征的支持向量机超鼻音检测的准确率分别为 86.89%、89.47%、90.57%和 91.70%，用于持续元音以及高压辅音 /k/、/p/和 /t/ 环境中的元音。