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Voice Pathology Detection Using Modulation Spectrum-Optimized Metrics.

Laureano Moro-Velázquez1, Jorge Andrés Gómez-García1, Juan Ignacio Godino-Llorente1

  • 1Center for Biomedical Technology, Universidad Politécnica de Madrid , Madrid , Spain.

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Summary
This summary is machine-generated.

Optimized modulation spectrum (MS) metrics improve pathological voice detection. Tuning these metrics across different ranges enhances their efficiency and generalization for clinical assessment.

Keywords:
EERcross-validationdysphoniamodulation spectrumspeech

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Area of Science:

  • Acoustic analysis
  • Speech pathology
  • Signal processing

Background:

  • Acoustic parameters are crucial for distinguishing normal and pathological voices.
  • Many existing parameters require specific tuning for optimal performance.
  • Modulation spectrum (MS) metrics offer potential for voice assessment but need optimization.

Purpose of the Study:

  • To optimize new and existing modulation spectrum (MS) metrics for pathological voice detection.
  • To identify optimal time and frequency ranges for MS metrics to maximize detection efficiency.
  • To evaluate the generalization capabilities of optimized MS metrics across different voice databases.

Main Methods:

  • Optimization of MS metrics across varied time and frequency ranges.
  • Simultaneous optimization using two distinct voice databases for cross-validation.
  • Validation of optimized metrics on a third, independent voice database.

Main Results:

  • Optimized MS metrics demonstrated similar tuning tendencies across databases, indicating good generalization.
  • The optimization process identified relevant modulation spectrum bands for specific metrics.
  • Achieved efficiency values reached up to 90.6% in one database and 71.1% in another.
  • Significant separability was observed between normophonic and pathological voice states.

Conclusions:

  • Optimized MS metrics show promise for effective voice pathology detection and assessment.
  • The tuning process provides insights into the physical interpretation of MS bands related to phonation.
  • The proposed MS metrics offer a robust tool for clinical voice analysis.