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Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody

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Selecting Disorder-Specific Features for Speech Pathology Fingerprinting.

Visar Berisha¹, Steven Sandoval², Rene Utianski¹

¹Department of Speech and Hearing Science, Arizona State University, Tempe, AZ 85287.

Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference)

|July 10, 2014

Summary

This summary is machine-generated.

This study introduces a new method to identify speech disorders by focusing on pathology-specific features, not individual speaking styles. This approach improves the accuracy of diagnosing conditions like dysarthria.

Keywords:

dysarthria feature selection machine learning speech pathology

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Area of Science:

Speech pathology
Biomedical signal processing
Machine learning for healthcare

Background:

Accurate identification of speech disorders is crucial for diagnosis and treatment.
Existing methods may struggle to differentiate between speaker-specific characteristics and pathology-related vocal changes.
Developing objective biomarkers for speech impairments remains a significant challenge.

Purpose of the Study:

To develop a novel feature selection algorithm for identifying unique statistical signatures of speech pathology.
To differentiate between speaker-specific vocal features and those indicative of a disorder, such as dysarthria.
To enhance the accuracy of speech disorder fingerprinting.

Main Methods:

Proposed a speaker identification approach tailored for dysarthric individuals.
Developed a novel feature selection algorithm to minimize speaker-specific variations (e.g., fundamental frequency) and maximize pathology-specific variations (e.g., vocal tract distortions, speech rhythm).
Derived and optimized a cost function for feature selection using an efficient algorithm tested on dysarthric and healthy speakers.

Main Results:

The proposed method successfully identified features directly related to the speech disorder, not individual speaking styles.
Demonstrated improved performance in a disorder fingerprinting task compared to other feature-selection algorithms.
The selected features were specific to the speech disorder, enhancing diagnostic potential.

Conclusions:

The novel feature selection algorithm effectively isolates pathology-specific acoustic markers for speech disorders.
This approach offers a more accurate and objective method for identifying and characterizing conditions like dysarthria.
The findings contribute to the advancement of computational methods in speech pathology and clinical diagnostics.