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Related Experiment Video

Updated: Mar 27, 2026

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

Published on: August 9, 2024

Prediction, Bayesian inference and feedback in speech recognition.

Dennis Norris¹, James M McQueen², Anne Cutler³

¹MRC Cognition and Brain Sciences Unit , Cambridge , UK.

Language, Cognition and Neuroscience

|January 8, 2016

Summary

This summary is machine-generated.

Listeners make predictions during speech perception, but simple activation feedback doesn't improve recognition. Optimal speech recognition relies on Bayesian inference, enabling adaptation to challenging auditory environments.

Keywords:

Bayesian inference Speech recognition feedback prediction

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Area of Science:

Cognitive Science
Auditory Neuroscience
Computational Linguistics

Background:

Speech perception relies on predictive mechanisms.
Cognitive models, like interactive-activation models (IAMs), often propose lexical-to-pre-lexical activation feedback for prediction.
The precise implementation and benefit of such feedback in speech recognition remain debated.

Purpose of the Study:

To investigate the role and effectiveness of activation feedback in speech perception.
To determine if simple activation feedback improves speech recognition accuracy.
To explore alternative feedback mechanisms that enhance speech recognition performance.

Main Methods:

Computational modeling of speech recognition processes.
Simulations comparing different feedback mechanisms within cognitive architectures.
Analysis of speech recognition performance under various auditory conditions.

Main Results:

Simple activation feedback from lexical to pre-lexical levels does not enhance speech recognition.
Alternative feedback mechanisms significantly improve speech recognition.
Beneficial feedback enables adaptation to changing input, unusual speech, and noisy environments.

Conclusions:

Speech perception prediction is crucial for optimal auditory processing.
Effective prediction in speech recognition is achieved through Bayesian inference, not simple activation feedback.
Bayesian models provide a framework for understanding how listeners adapt and succeed in complex listening situations.