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Automated Detection of Vocalizations in Laryngeal Videostroboscopy
Ying-Chang Wu1, Ming-Chi Cheng1, Sheng-Fu Liang1
1Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan, R.O.C.
Studies in Health Technology and Informatics
|August 8, 2025
Summary
This study introduces an automated system for analyzing vocal fold vibration using spectrograms and cosine similarity. The voice activity detection method achieves over 97% accuracy, improving glottal area analysis in clinical settings.
Area of Science:
- Phonatory medicine
- Computational bioacoustics
- Medical imaging analysis
Background:
- Glottal area (GA) analysis is crucial for understanding vocal fold vibration in phonatory medicine.
- Quantitative vocal fold characteristics from endoscopic images aid diagnostic accuracy.
- Current methods may lack efficiency in identifying relevant vocalization areas.
Purpose of the Study:
- To develop an automated computational system for videostroboscopy using spectrograms and cosine similarity.
- To efficiently identify vocalization areas for further clinical analysis.
- To enhance the speed and accuracy of diagnosing vocal fold pathologies.
Main Methods:
- Development of an automated computational system for videostroboscopy.
- Leveraging spectrograms and cosine similarity techniques for analysis.
- Implementation of a Voice Activity Detection (VAD) method.
Main Results:
- The proposed Voice Activity Detection (VAD) method achieved over 97% precision and recall.
- 5-fold cross-validation confirmed the robustness of the VAD method.
- The system efficiently identifies vocalization areas for clinical review.
Conclusions:
- The automated system offers a rapid and accurate method for analyzing vocal fold vibration.
- This technique can significantly improve diagnostic capabilities in phonatory medicine.
- Healthcare providers can achieve better diagnostic outcomes by visualizing specific vocal fold vibration regions more effectively.
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