Statistical learning of phonetic categories: insights from a computational approach

Abstract

Recent evidence (Maye, Werker & Gerken, 2002) suggests that statistical learning may be an important mechanism for the acquisition of phonetic categories in the infant's native language. We examined the sufficiency of this hypothesis and its implications for development by implementing a statistical learning mechanism in a computational model based on a Mixture of Gaussians (MOG) architecture. Statistical learning alone was found to be insufficient for phonetic category learning-an additional competition mechanism was required in order to successfully learn the categories in the input. When competition was added to the MOG architecture, this class of models successfully accounted for developmental enhancement and loss of sensitivity to phonetic contrasts. Moreover, the MOG with competition model was used to explore a potentially important distributional property of early speech categories --sparseness --in which portions of the space between phonetic categories is unmapped. Sparseness was found in all successful models and quickly emerged during development even when the initial parameters favored continuous representations with no gaps. The implications of these models for phonetic category learning in infants are discussed.Infants face a difficult problem in acquiring their native language because the acoustic/ phonetic variability in the input far exceeds the limited number of distinctive differences that define language-specific phonemes. How do infants attend to the relevant information that distinguishes words? Recent evidence suggests that phonemic categories may be induced, in whole or in part, by a rapid statistical learning mechanism that is sensitive to the distributional properties of phonetic input (Maye, Werker & Gerken, 2002;Maye, Weiss & Aslin, 2008). This evidence suggests that the detailed frequency-of-occurrence of tokens along continuous speech dimensions plays a crucial role in the formation and modification of phonemic categories.The present paper describes a computational model of statistical speech category learning that examines the necessary and sufficient mechanisms needed to account for known empirical data from infants, and the implications of those mechanisms for early speech categories. We demonstrate that statistical learning alone is insufficient: competition is also required. However, once this feature is added to the model, it can account for a number of developmental trajectories in speech category learning. Finally, we examine the possibility Statistical Learning and DevelopmentThe classic view of speech perception in both adults (cf., Liberman, Harris, Hoffman & Griffith, 1957) and infants (cf., Eimas, Siqueland, Jusczyk & Vigorito, 1971; see Jusczyk, 1997) is that stop consonants are perceived categorically. However, more recent evidence confirms within-category sensitivity in both adults (Pisoni & Tash, 1974; Carney, Widen & Viemeister, 1979;Miller, 1997) and infants (Miller & Eimas, 1996;McMurray & Aslin, 2005). Nevertheless, adults and infants have a bias t...

Semantics guide infants’ vowel learning: Computational and experimental evidence

“…Recent models have tested whether infant-directed speech indeed contains sufficiently clear peaks for such a distributional learning mechanism to succeed. Indeed, this appears to be the case for both consonants (at least for VOT contrasts, McMurray, Aslin, & Toscano, 2009) and vowels (Vallabha, McClelland, Pons, Werker, & Amano, 2007;Benders, 2013). In short, computational models of first language acquisition provide evidence that infants' input contains sufficient information to learn phonetic contrasts without requiring lexical knowledge.…”

Multi-level processing of phonetic variants in speech production and visual word processing: evidence from Mandarin lexical tones

“…Accounts based purely on statistical distributions of acoustic cues would have difficulty explaining the formation of separate categories in such cases where acoustic information from two speech categories is very similar. Recent evidence from computational models also suggests that acoustic distributional information alone may not be sufficient for phonetic category acquisition (Feldman, Myers, White, Griffiths, & Morgan, 2011;McMurray, Aslin, & Toscano, 2009a). Feldman et al (2011) suggest that phonetic category development occurs as part of extracting meaning from language, through association of phonetic distributions with lexical items.…”

Weak semantic context helps phonetic learning in a model of infant language acquisition

“…Infants attend to distributional characteristics of their input (Maye et al, 2002(Maye et al, , 2008, leading to the hypothesis that phonetic categories could be acquired on the basis of bottom-up distributional learning alone (de Boer and Kuhl, 2003;Vallabha et al, 2007;McMurray et al, 2009). However, this would require sound categories to be well separated, which often is not the case-for example, see Figure 1, which shows the English vowel space that is the focus of this paper.…”

“…Following previous models of vowel learning (de Boer and Kuhl, 2003;Vallabha et al, 2007;McMurray et al, 2009;Dillon et al, 2013) we assume that vowel tokens are drawn from a Gaussian mixture model. The Infinite Gaussian Mixture Model (IGMM) (Rasmussen, 2000) includes a DP prior, as described above, in which the base distribution H C generates multivariate Gaussians drawn from a Normal Inverse-Wishart prior.…”

“…In theory, semantic information could offer a valuable cue for phoneme induction 1 by helping infants distinguish between minimal pairs, as linguists do (Trubetzkoy, 1939). However, due to a widespread assumption that infants do not know the meanings of many words at the age when they are learning phonetic categories (see Swingley, 2009 for a review), most recent models of early phonetic category acquisition have explored the phonetic learning problem in the absence of semantic information (de Boer and Kuhl, 2003;Dillon et al, 2013;Feldman et al, 2013a;McMurray et al, 2009;Vallabha et al, 2007).…”