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Acoustic speech analysis for hypernasality detection in children.

G Castellanos¹, G Daza, L Sánchez

¹Universidad Nacional de Colombia Sede Manizales. cgcastellanos@unal.edu.co

Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference

|October 20, 2007

Summary

This study identifies acoustic features for precise automatic detection of hypernasality. The proposed method requires less computational power and no training samples for effective speech analysis.

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Area of Science:

Speech Science
Biomedical Engineering
Computational Linguistics

Background:

Hypernasality, a speech disorder, poses challenges for accurate diagnosis.
Automatic identification of hypernasality relies on analyzing acoustic features of speech.
Existing methods may require extensive training data and significant computational resources.

Purpose of the Study:

To analyze acoustic features for effective automatic identification of hypernasality.
To develop a specialized diagnostic feature for hypernasal speech.
To reduce computational requirements and eliminate the need for training samples in hypernasality detection.

Main Methods:

Preprocessing of acoustic features using statistical independence analysis.
Synthesis of a novel diagnostic feature by analyzing acoustic emissions in hypernasal speech.
Evaluation of feature effectiveness in differentiating hypernasality from normal speech.

Main Results:

Acoustic features demonstrate sufficient precision in differentiating hypernasality.
The proposed diagnostic feature effectively identifies the pathology.
The new feature requires less computational power and no training samples.

Conclusions:

Acoustic feature analysis is a viable method for automatic hypernasality identification.
The developed diagnostic feature offers an efficient and accessible approach to detecting hypernasality.
This method has the potential to improve diagnostic tools for speech disorders.