An Efficient, Probabilistically Sound Algorithm for Segmentation and Word Discovery

This paper presents a model-based, unsupervised algorithm for recovering word boundaries in a natural-language text from which they have been deleted. The algorithm is derived from a probability model of the source that generated the text. The fundamental structure of the model is specified abstractly so that the detailed component models of phonology, word-order, and word frequency can be replaced in a modular fashion. The model yields a language-independent, prior probability distribution on all possible sequences of all possible words over a given alphabet, based on the assumption that the input was generated by concatenating words from a fixed but unknown lexicon. The model is unusual in that it treats the generation of a complete corpus, regardless of length, as a single event in the probability space. Accordingly, the algorithm does not estimate a probability distribution on words; instead, it attempts to calculate the prior probabilities of various word sequences that could underlie the observed text. Experiments on phonemic transcripts of spontaneous speech by parents to young children suggest that this algorithm is more effective than other proposed algorithms, at least when utterance boundaries are given and the text includes a substantial number of short utterances. Keywords: Bayesian grammar induction, probability models, minimum description length (MDL), unsupervised learning, cognitive modeling, language acquisition, segmentation

Segmenting speech without a lexicon: The roles of phonotactics and speech source

Infants face the difficult problem of segmenting continuous speech into words without the benefit of a fully developed lexicon. Several sources of information in speech might help infants solve this problem, including prosody, semantic correlations and phonotactics. Research to date has focused on determining to which of these sources infants might be sensitive, but little work has been done to determine the potential usefulness of each source. The computer simulations reported here are a first attempt to measure the usefulness of distributional and phonotactic information in segmenting phoneme sequences. The algorithms hypothesize different segmentations of the input into words and select the best hypothesis according to the Minimum Description Length principle. Our results indicate that while there is some useful information in both phoneme distributions and phonotactic rules, the combination of both sources is most useful.