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Praditor: A DBSCAN-based automation for speech onset detection.

Zhengyuan Liu1, Xinqi Yu2,3, Wing Chung Hu4

  • 1Centre for Cognitive and Brain Sciences, University of Macau, Taipa, Macau SAR, China.

Behavior Research Methods
|August 4, 2025
PubMed
Summary
This summary is machine-generated.

Praditor, a new tool, accurately detects speech onset time (SOT) using automated algorithms, reducing manual annotation effort. This semi-automatic approach aids speech production research across languages.

Keywords:
Automatic detectionDBSCANFirst derivative thresholdingLanguage productionSpeech onset time

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Area of Science:

  • Linguistics
  • Speech Science
  • Computational Linguistics

Background:

  • Speech onset time (SOT) is crucial for speech production research.
  • Manual SOT annotation is accurate but time-consuming and fatiguing.
  • Existing methods may lack user-friendliness or language independence.

Purpose of the Study:

  • Introduce Praditor, a semi-automatic tool for speech onset detection.
  • Provide a user-friendly and efficient solution for SOT annotation.
  • Facilitate language-independent speech analysis.

Main Methods:

  • Developed Praditor using DBSCAN and first-derivative thresholding algorithms.
  • Implemented a graphical user interface (GUI) for easy parameter tuning.
  • Tested on Mandarin and English datasets for accuracy assessment.

Main Results:

  • Praditor demonstrated high accuracy, with ~90% of annotations within ±20 ms of manual annotations.
  • Both file-level and corpus-level tuning showed acceptable accuracy.
  • The tool processed single-onset and multiple-onset audio files effectively.

Conclusions:

  • Praditor offers a reliable and efficient semi-automatic solution for speech onset annotation.
  • The tool is language-independent and accessible to users with varying technical expertise.
  • Praditor is openly available, promoting broader use in speech research.