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An Investigation of Multidimensional Voice Program Parameters in Three Different Databases for Voice Pathology
Ahmed Al-Nasheri1, Ghulam Muhammad1, Mansour Alsulaiman1
1Digital Speech Processing Group, Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia.
Journal of Voice : Official Journal of the Voice Foundation
|April 24, 2016
Summary
This study investigated Multidimensional Voice Program (MDVP) parameters for automatic voice pathology detection. Results show varying parameter performance across languages, with top parameters achieving high accuracy in classifying voice disorders.
Area of Science:
- Speech science
- Medical informatics
- Computational linguistics
Background:
- Automatic voice pathology detection aids early diagnosis.
- Multidimensional Voice Program (MDVP) parameters are key features for analysis.
- Cross-linguistic validation is crucial for robust systems.
Purpose of the Study:
- To investigate MDVP parameters for automatic voice pathology detection and classification.
- To compare the performance of MDVP parameters across different language databases.
- To identify optimal parameters for accurate voice disorder identification.
Main Methods:
- Utilized sustained vowel /a/ samples from Arabic, English, and German voice pathology databases.
- Extracted MDVP parameters using a computerized speech lab program for acoustical analysis.
- Employed Fisher discrimination ratio for parameter ranking and t-tests for significance.
Main Results:
- MDVP parameter performance varied significantly across the three databases.
- Top-ranked parameters differed depending on the language database.
- Highest accuracies (99.68%, 88.21%, 72.53%) were achieved using the top three parameters for German, English, and Arabic databases, respectively.
Conclusions:
- MDVP parameter effectiveness in voice pathology detection is database-dependent.
- Language-specific acoustic characteristics influence parameter performance.
- Further research is needed to develop universally applicable voice pathology detection models.
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