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Modelling representations in speech normalization of prosodic cues.

Chen Si1,2,3, Caicai Zhang4,5,6, Puiyin Lau7

  • 1Department of Chinese and Bilingual Studies, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China. sarahchen@polyu.edu.hk.

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This summary is machine-generated.

This study shows how our brains use stored speech sound patterns to understand different voices. This helps in speech perception, even with unfamiliar talkers and varying contexts.

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Area of Science:

  • Psycholinguistics
  • Speech Perception
  • Computational Auditory Processing

Background:

  • The lack of invariance problem highlights challenges in understanding speech due to speaker variability.
  • Previous research has not fully explored the role of mentally stored distributional information in speech normalization.

Purpose of the Study:

  • To investigate the contribution of distributional information to the normalization of prosodic cues in speech perception.
  • To propose and validate a novel probabilistic parametric representation (PPR) model for speech normalization.

Main Methods:

  • Modeling acoustic cue distributions from a speech corpus.
  • Conducting three experiments with naturally produced and manipulated lexical tones.
  • Employing advanced statistical techniques to analyze the effects of distribution parameters on normalization and identification.

Main Results:

  • Distribution parameters significantly influence speech normalization and identification.
  • The proposed probabilistic parametric representation (PPR) integrates speaker distribution and indexical information effectively.
  • PPR remains accessible during speech perception, even with contextual information present.

Conclusions:

  • Mentally stored distributional information plays a crucial role in normalizing prosodic cues.
  • The probabilistic parametric representation (PPR) offers a new framework for understanding speech normalization.
  • This model accounts for how listeners process speech from both familiar and unfamiliar talkers, with or without context.