A computational model for unsupervised word discovery

Abstract

We present an unsupervised algorithm for the discovery of words and word-like fragments from the speech signal, without using an upfront defined lexicon or acoustic phone models. The algorithm is based on a combination of acoustic pattern discovery, clustering, and temporal sequence learning. It exploits the acoustic similarity between multiple acoustic tokens of the same words or word-like fragments. In its current form, the algorithm is able to discover words in speech with low perplexity (connected digits). Although its performance still falls off compared to mainstream ASR approaches, the value of the algorithm is its potential to serve as a computational model in two research directions. First, the algorithm may lead to an approach for speech recognition that is fundamentally liberated from the modelling constraints in conventional ASR. Second, the proposed algorithm can be interpreted as a computational model of language acquisition that takes actual speech as input and is able to find words as ’emergent’ properties from raw input.