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Computational Modelling of Tone Perception Based on Direct Processing of f0 Contours.

Yue Chen1, Yingming Gao2, Yi Xu1

  • 1Department of Speech, Hearing and Phonetic Sciences, University College London, London WC1N 1PF, UK.

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Summary

Speech perception may not require feature detection before recognizing sounds. Computational modeling suggests direct processing of acoustic signals, like Mandarin tones, is more efficient than extracting intermediate features. This challenges traditional speech perception models.

Keywords:
Mandarin tonesspeech perceptiontone featurestone recognition

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Area of Science:

  • Psychology
  • Linguistics
  • Computational Neuroscience

Background:

  • Traditional speech perception models emphasize feature detection before phonetic unit recognition.
  • The exact mechanism of feature extraction in continuous speech perception remains unclear.
  • Auditory cues and articulatory gestures are considered potential features.

Purpose of the Study:

  • To computationally model speech perception without intermediate featural representations.
  • To investigate if phonetic categories can be recognized directly from continuous acoustic signals.
  • To explore the operational mechanism of tone perception in Mandarin.

Main Methods:

  • Utilized computational modeling with Support Vector Machine (SVM) and Self-organizing Map (SOM).
  • Simulated Mandarin tone perception by directly processing fundamental frequency (f0) trajectories.
  • Compared direct tone recognition with schemes involving tonal feature extraction.

Main Results:

  • Direct tone recognition outperformed all tested feature extraction methods.
  • Direct recognition required less computational power compared to feature-based approaches.
  • The findings challenge the necessity of prior feature extraction in speech perception.

Conclusions:

  • Prior extraction of features may not be the operational mechanism in speech perception.
  • Direct processing of acoustic signals is a viable and efficient method for phonetic recognition.
  • This study provides computational evidence against traditional feature-based speech perception theories.