Literature DB >> 29857767

Detection of hypernasality based on vowel space area.

Akhilesh Kumar Dubey1, Ayush Tripathi2, S R M Prasanna1, S Dandapat1.   

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.

Mesh:

Year:  2018        PMID: 29857767     DOI: 10.1121/1.5039718

Source DB:  PubMed          Journal:  J Acoust Soc Am        ISSN: 0001-4966            Impact factor:   1.840


  1 in total

1.  Robust Estimation of Hypernasality in Dysarthria with Acoustic Model Likelihood Features.

Authors:  Michael Saxon; Ayush Tripathi; Yishan Jiao; Julie Liss; Visar Berisha
Journal:  IEEE/ACM Trans Audio Speech Lang Process       Date:  2020-08-07
  1 in total

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