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Related Experiment Video

Updated: Mar 18, 2026

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
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Automatic Measurement of Voice Onset Time and Prevoicing using Recurrent Neural Networks.

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This study introduces a new algorithm for measuring voice onset time (VOT), including negative VOT found in many languages. The algorithm accurately detects prevoicing and measures VOT for stop consonants.

Keywords:
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Area of Science:

  • Phonetics and Speech Science
  • Computational Linguistics
  • Acoustic Phonetics

Background:

  • Voice onset time (VOT) is a crucial phonetic feature differentiating stop consonants.
  • Existing automatic VOT measurement methods primarily focus on positive VOT, neglecting negative VOT prevalent in many languages.
  • Accurate measurement of VOT, including prevoicing, is essential for speech analysis and linguistic research.

Purpose of the Study:

  • To develop and evaluate an algorithm for automatic measurement of voice onset time (VOT), encompassing both positive and negative values.
  • To accurately detect prevoicing events and measure VOT for single stop consonants in speech segments.
  • To improve upon the state-of-the-art in automatic VOT measurement and prevoicing detection.

Main Methods:

  • A recurrent neural network (RNN) was trained on manually labeled speech data.
  • The RNN models the dynamic temporal characteristics of speech signals to identify acoustic events.
  • The algorithm processes speech segments to output burst onset, burst duration, and prevoicing onset with confidence scores.

Main Results:

  • The proposed algorithm demonstrates superior performance in measuring VOT compared to existing methods.
  • The algorithm achieves high accuracy in detecting prevoicing events.
  • The system effectively measures both positive and negative VOT values.

Conclusions:

  • The developed algorithm offers a robust solution for automatic VOT measurement, including negative VOT and prevoicing.
  • This advancement has significant implications for phonetic analysis and cross-linguistic speech research.
  • The RNN-based approach provides a reliable method for analyzing temporal dynamics in speech signals.