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Detection of hypernasality based on vowel space area.

Akhilesh Kumar Dubey1, Ayush Tripathi2, S R M Prasanna1

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Summary

This study shows that reduced vowel space area (VSA) can differentiate hypernasal speech from normal speech. Combining VSA with Mel-frequency cepstral coefficients improves hypernasality detection accuracy.

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Area of Science:

  • Speech Science
  • Acoustic Phonetics
  • Speech Pathology

Background:

  • Hypernasal speech, characterized by abnormal nasal resonance, affects speech intelligibility.
  • Acoustic correlates of hypernasality, such as formant shifts, are not fully understood.
  • Objective methods for hypernasality detection are needed.

Purpose of the Study:

  • To propose and evaluate a method for differentiating hypernasal speech from normal speech using vowel space area (VSA).
  • To assess the effectiveness of VSA as a feature for hypernasality detection.

Main Methods:

  • Acoustic analysis of vowel spectra to identify formant and anti-formant shifts.
  • Calculation of vowel space area (VSA) for normal and hypernasal speech samples.
  • Support vector machine (SVM) classification using VSA and Mel-frequency cepstral coefficients (MFCCs).

Main Results:

  • Vowel space area (VSA) was found to be significantly reduced in hypernasal speech compared to normal speech.
  • The proposed method achieved high detection accuracies: 86.89% for sustained vowels and up to 91.70% for vowels in consonant contexts.
  • Combined VSA and MFCC features enhanced classification performance.

Conclusions:

  • Vowel space area (VSA) is a viable acoustic feature for differentiating hypernasal speech.
  • The integration of VSA with MFCCs offers a robust approach for automated hypernasality detection.
  • This method has potential applications in speech diagnostics and therapy.