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Acoustic analysis and detection of hypernasality using a group delay function
P Vijayalakshmi1, M Ramasubba Reddy, Douglas O'Shaughnessy
1Biomedical Engineering Division, Indian Institute of Technology, Madras 600 036, India. pviayalakshmi@iitm.ac.in
IEEE Transactions on Bio-Medical Engineering
|April 5, 2007
Summary
This study introduces a novel group delay-based signal processing technique for detecting hypernasal speech. The method accurately identifies hypernasality by focusing on low-frequency acoustic changes, achieving high detection rates across different vowels.
Area of Science:
- Speech processing
- Acoustic analysis
- Speech pathology
Background:
- Hypernasality, a speech disorder, results from abnormal airflow through the nasal cavity.
- Traditional acoustic analysis faces challenges in resolving closely spaced formants characteristic of hypernasal speech.
- A consistent low-frequency resonance around 250 Hz is observed in hypernasal speech.
Purpose of the Study:
- To develop and validate a novel group delay-based signal processing technique for hypernasality detection.
- To introduce a new acoustic measure derived from the band-limited group delay spectrum.
- To assess the effectiveness of the proposed measure across different vowel phonemes.
Main Methods:
- A group delay-based signal processing technique was employed.
- A band-limited approach was developed to estimate formant locations in hypernasal speech.
- A new acoustic measure was defined using the band-limited group delay spectrum.
Main Results:
- The proposed acoustic measure achieved 100% detection accuracy for the phoneme /a/.
- Detection accuracies for /i/ and /u/ were 88.78% and 86.66%, respectively.
- The method's effectiveness was validated on diverse speech data from different recording environments.
Conclusions:
- The group delay-based acoustic measure is a promising tool for accurate hypernasality detection.
- The low-frequency acoustic characteristics of hypernasal speech are key indicators for detection.
- The developed technique offers a robust solution for analyzing and identifying hypernasal speech patterns.

