Connectionist models of speech segmentation and the utterance boundary strategy: A comparison of the SOM, SRN and N-grams

Some connectionist models of speech segmentation have exploited the utterance boundary strategy, where the fact that utterance endings are also word endings is used to infer where word boundaries are. In this paper, it is demonstrated that using a simple N-gram based approach outperforms the neural networks for bigrams and especially for trigrams. Moreover the trigrams performance is better than that reported for all but the best 3 unsupervised models of speech segmentation in the literature. The implications of these findings both for connectionist models of segmentation and for the cognitive modelling of segmentation more generally are discussed.