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Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language
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Glottal Gap tracking by a continuous background modeling using inpainting.

Gustavo Andrade-Miranda1, Juan Ignacio Godino-Llorente2

  • 1Center for Biomedical Technology, Universidad Politécnica de Madrid, Campus de Montegancedo, Crta. M40 km, 38, Madrid, Spain. gxandrade@ics.upm.es.

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Summary
This summary is machine-generated.

This study introduces a quasi-automatic framework for segmenting the glottal area, improving vocal fold vibration analysis for diagnosing voice disorders. The new method enhances accuracy and reduces errors in detecting vocal fold closure.

Keywords:
Background subtractionGlottal gap segmentationHigh speed imagingRegion of interestSpecularity removal

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Area of Science:

  • Biomedical Engineering
  • Acoustics
  • Speech Science

Background:

  • Visual examination of vocal fold vibration patterns is crucial for understanding phonation and diagnosing voice disorders.
  • Manual or semi-automatic segmentation of the glottal area is a bottleneck, being difficult and time-consuming.

Purpose of the Study:

  • To present a quasi-automatic framework for accurate glottal area segmentation.
  • To introduce novel techniques not previously explored in the state-of-the-art for this task.
  • To allow minimal user intervention for cases where automatic computation fails.

Main Methods:

  • Development of a quasi-automatic segmentation framework for the glottal area.
  • Integration of several novel techniques into the segmentation process.
  • Incorporation of minimal user intervention for improved accuracy.

Main Results:

  • Achieved reliable delimitation of the glottal gap.
  • Demonstrated an average improvement of 13% and 18% compared to existing approaches.
  • Reduced errors in detecting total vocal fold closure instants.

Conclusions:

  • The proposed framework offers a significant advancement in glottal area segmentation accuracy and efficiency.
  • The developed validation guidelines can help standardize accuracy and efficiency criteria for segmentation algorithms.
  • This method facilitates more precise analysis of phonation and voice disorder diagnosis.