Speech perception: a model of word recognition

TL;DR

This paper introduces a speech perception model based on attractor dynamics that accounts for correlations between sounds, effectively recognizing words of varying lengths and handling mishearings with different strategies for short and long words.

Contribution

It presents a novel dynamical model of speech perception that incorporates sound correlations and analyzes word recognition behavior for different word lengths.

Findings

Short words are recognized quickly or rejected as alternative words.

Long words can be recognized or lead to permanent misrecognition due to wandering.

The model reflects realistic word length distributions and error handling.

Abstract

We present a model of speech perception which takes into account effects of correlations between sounds. Words in this model correspond to the attractors of a suitably chosen descent dynamics. The resulting lexicon is rich in short words, and much less so in longer ones, as befits a reasonable word length distribution. We separately examine the decryption of short and long words in the presence of mishearings. In the regime of short words, the algorithm either quickly retrieves a word, or proposes another valid word. In the regime of longer words, the behaviour is markedly different. While the successful decryption of words continues to be relatively fast, there is a finite probability of getting lost permanently, as the algorithm wanders round the landscape of suitable words without ever settling on one.