Unsupervised Extraction of Recurring Words from Infant-Directed Speech

Unsupervised Extraction of Recurring Words from Infant-Directed Speech Fergus R. McInnes (Fergus.McInnes@ed.ac.uk) and Sharon J. Goldwater (sgwater@inf.ed.ac.uk) School of Informatics, University of Edinburgh, 10 Crichton Street, Edinburgh EH8 1AB, UK Abstract To date, most computational models of infant word segmenta- tion have worked from phonemic or phonetic input, or have used toy datasets. In this paper, we present an algorithm for word extraction that works directly from naturalistic acoustic input: infant-directed speech from the CHILDES corpus. The algo- rithm identifies recurring acoustic patterns that are candidates for identification as words or phrases, and then clusters together the most similar patterns. The recurring patterns are found in a single pass through the corpus using an incremental method, where only a small number of utterances are considered at once. Despite this limitation, we show that the algorithm is able to extract a number of recurring words, including some that infants learn earliest, such as Mommy and the child’s name. We also introduce a novel information-theoretic evaluation measure. Keywords: language acquisition; word segmentation; speech recognition; computational modelling. Introduction One of the first problems children face in learning language is how to segment individual words from the continuous stream of acoustic input they hear. Experimental evidence suggests that infants are sensitive to the statistical patterns created by strings of words in a nonsense language (Saffran, Newport, & Aslin, 1996), and can use these statistical cues to distinguish words from non-words at an earlier age than other cues such as stress (Thiessen & Saffran, 2003). Since this evidence be- gan to surface, a number of computational models have been proposed to explain how infants might exploit the statistical information in the speech input in order to identify words or word boundaries (e.g., Brent, 1999; Christiansen, Allen, & Seidenberg, 1998; Goldwater, Griffiths, & Johnson, 2009; Rytting, 2007). Nearly all such models have assumed a string of phonemic or phonetic symbols as input, though recently some researchers have begun to explore the problem of word identification in a less idealized scenario, using acoustic fea- ture vectors as input (Aimetti, 2009; Driesen, ten Bosch, & Van hamme, 2009; R¨ as¨ anen, 2010). The work described here falls into the latter category, and is motivated by evidence that infants begin to segment some words as early as six months old, while their native language phonology is still incomplete (Bortfeld, Morgan, Golinkoff, & Rathbun, 2005). This sug- gests the possibility that some early words may be learned as a whole from the acoustic signal, without any sub-word level of representation. The algorithm we present is similar to that of Aimetti (2009), in that both are based on recent work by Park and Glass (2008) (henceforth, PG quantitative evaluation was slow, limited in scope, and often subjective because it required examining the output by hand. An exception is presented by Jansen, Church, and Her- mansky (2010), but their method depends on an information- retrieval task. Our evaluation method can be applied automat- ically (assuming a phonemic forced alignment is available), which makes quantitative comparisons between different con- ditions or learning algorithms much easier. In the remainder of the paper, we first present the pattern extraction algorithm and the evaluation measure. We then de- scribe our experiments, presenting both qualitative and quanti- tative results. We conclude with a discussion of our findings and directions for further work. Pattern Extraction Algorithm Our algorithm is a modification of the segmental dynamic time warping (DTW) algorithm of PG and clustering, in which the matched fragments are grouped together across the whole set of utterances using a graph-based clustering method. In P&G’s original algorithm, every utter-