Learning phonetic categories by learning a lexicon

Learning Phonetic Categories by Learning a Lexicon Naomi H. Feldman (naomi feldman@brown.edu) Department of Cognitive and Linguistic Sciences, Brown University, Providence, RI 02912 USA Thomas L. Griffiths (tom griffiths@berkeley.edu) Department of Psychology, University of California at Berkeley, Berkeley, CA 94720 USA James L. Morgan (james morgan@brown.edu) Department of Cognitive and Linguistic Sciences, Brown University, Providence, RI 02912 USA Abstract Infants learn to segment words from fluent speech during the same period as they learn native language phonetic cate- gories, yet accounts of phonetic category acquisition typically ignore information about the words in which speech sounds appear. We use a Bayesian model to illustrate how feed- back from segmented words might constrain phonetic category learning, helping a learner disambiguate overlapping phonetic categories. Simulations show that information from an artifi- cial lexicon can successfully disambiguate English vowel cat- egories, leading to more robust category learning than distri- butional information alone. Keywords: language acquisition; Bayesian inference phonetic categories; Infants learning their native language need to extract sev- eral levels of structure, including the locations of phonetic categories in perceptual space and the identities of words they segment from fluent speech. It is often implicitly as- sumed that these steps occur sequentially, with infants first learning about the phonetic categories in their language and subsequently using those categories to help them map word tokens onto lexical items. However, infants begin to segment words from fluent speech as early as 6 months (Bortfeld, Mor- gan, Golinkoff, & Rathbun, 2005) and this skill continues to develop over the next several months (Jusczyk & Aslin, 1995; Jusczyk, Houston, & Newsome, 1999). Discrimina- tion of non-native speech sound contrasts declines during the same time period, between 6 and 12 months (Werker & Tees, 1984). This suggests an alternative learning trajectory in which infants simultaneously learn to categorize both speech sounds and words, potentially allowing the two learning pro- cesses to interact. In this paper we explore the hypothesis that the words in- fants segment from fluent speech can provide a useful source of information for phonetic category acquisition. We use a Bayesian approach to explore the nature of the phonetic cat- egory learning problem in an interactive system, where infor- mation from segmented words can feed back and constrain phonetic category learning. Our interactive model learns a rudimentary lexicon and a phoneme inventory 1 simulta- neously, deciding whether acoustic representations of seg- mented tokens correspond to the same or different lexical items (e.g. bed vs. bad) and whether lexical items contain 1 We make the simplifying assumption that phonemes are equiv- alent to phonetic categories, and use the terms interchangeably. the same or different vowels (e.g. send vs. act). Simulations demonstrate that using information from segmented words to constrain phonetic category acquisition allows more robust category learning from fewer data points, due to the inter- active learner’s ability to use information about which words contain particular speech sounds to disambiguate overlapping categories. The paper is organized as follows. We begin with an intro- duction to the mathematical framework for our model, then present toy simulations to demonstrate its qualitative proper- ties. Next, simulations show that information from an artifi- cial lexicon can disambiguate formant values associated with English vowel categories. The last section discusses potential implications for language acquisition, revisits the model’s as- sumptions, and suggests directions for future research. Bayesian Model of Phonetic Category Learning Recent research on phonetic category acquisition has focused on the importance of distributional learning. Maye, Werker, and Gerken (2002) found that the specific frequency dis- tribution (bimodal or unimodal) of speech sounds along a continuum could affect infants’ discrimination of the contin- uum endpoints, with infants showing better discrimination of the endpoints when familiarized with the bimodal distribu- tion. This work has inspired computational models that use a Mixture of Gaussians approach, assuming that phonetic cate- gories are represented as Gaussian, or normal, distributions of speech sounds and that learners find the set of Gaussian cat- egories that best represents the distribution of speech sounds they hear. Boer and Kuhl (2003) used the Expectation Max- imization (EM) algorithm (Dempster, Laird, & Rubin, 1977) to learn the locations of three such vowel categories from for- mant data. McMurray, Aslin, and Toscano (2009) introduced a gradient descent algorithm similar to EM to learn a stop consonant voicing contrast, and this algorithm has been ex- tended to multiple dimensions for both consonant and vowel data (Toscano & McMurray, 2008; Vallabha, McClelland, Pons, Werker, & Amano, 2007). Our model adopts the Mixture of Gaussians approach from these previous models but uses a non-parametric Bayesian framework that allows extension of the model to the word level, making it possible to investigate the learning outcome when multiple levels of structure interact. As in previous models, speech sounds in our model are represented using