Speech perception: a model of word recognition
This work addresses speech perception for computational linguistics and cognitive science, but it appears incremental as it builds on existing models by incorporating sound correlations and analyzing word length effects.
The authors tackled speech perception by developing a model of word recognition that accounts for sound correlations, using attractors in a descent dynamics to form a lexicon with realistic word length distributions. They found that short words are either quickly retrieved or replaced by another valid word, while longer words have a finite probability of never settling on a single word during decryption.
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.