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Lecture4-SpeechPerception2

Lecture4-SpeechPerception2 - Speech Perception...

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Psych 215L: Language Acquisition Lecture 4 Speech Perception Speech Perception: Computational Problem Divide sounds into contrastive categories x x C1 C2 C3 C4 x x x x x x x x x x x x x x x x x x x x Speech Perception: Computational Problem Remember that real world data are actually much harder than this… (from Swingley 2009) Order of acquisition? “It is often implicitly assumed…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, Morgan, Golinkoff, & Rathbun, 2005) and this skill continues to develop over the next several months (Jusczyk & Aslin, 1995; Jusczyk, Houston, & Newsome, 1999). Discrimination 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 processes to interact.”
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What we know about infants Maye, Werker, & Gerken 2002: infants show sensitivity to statistical distribution of acoustic data points Mixture of Gaussians (MoGs) modeling approaches building on this ability: - Boer and Kuhl 2003: Expectation Maximization (EM) algorithm (Dempster, Laird, & Rubin 1977) to learn the locations of three vowel categories from formant data. - Toscano & McMurray 2008, Vallabha et al. 2007: EM to learn multiple dimensions for both consonant and vowel data - McMurray, Aslin, and Toscano 2009: gradient descent algorithm similar to EM to learn a stop consonant voicing contrast. Feldman, Griffiths, & Morgan 2009 Use MoG approach within a non-parametric Bayesian framework. Why? Allows extension of the model to the word level (instead of only including the phonemic category level). Phonetic dimensions used to describe input data: - formant values (F1, F2) - voice onset time Words: Sequences of phonetic values, where each phoneme corresponds to a discrete set of phonetic values F1: depends on whether the sound is more open or closed. (Varies along y axis.) F1 increases as the vowel becomes more open and decreases as vowel closes. F2: depends on whether the sound is made in the front or the back of the vocal cavity. (Varies along x axis). F2 increases the more forward the sound is. Idea: As long as speakers use the same values for these formants, they will produce the same vowel. Formants High F1 Low F1 High F2 Low F2 Sample Input Input Stream: ADAABDABDC ADA AB D AB DC Learner’s job is to recover (1) A, B, C, D distributions (2) words ADA, AB, D, AB, and DC
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Distributional Model Model goal: learn the phoneme inventory (ignore information about words and word boundaries) Phoneme inventory = {A, B, C, D, …} Sounds are assumed to be produced by the speaker selecting a category from the phoneme inventory and then sampling a phonetic value from the Gaussian associated with that category.
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