Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/7191
Title: The Unsupervised Acquisition of a Lexicon from Continuous Speech
Keywords: AI
MIT
Artificial Intelligence
induction
unsupervised learning
language acquisition
lexical acquisition
continuous speech
Issue Date: 9-Oct-2013
Description: We present an unsupervised learning algorithm that acquires a natural-language lexicon from raw speech. The algorithm is based on the optimal encoding of symbol sequences in an MDL framework, and uses a hierarchical representation of language that overcomes many of the problems that have stymied previous grammar-induction procedures. The forward mapping from symbol sequences to the speech stream is modeled using features based on articulatory gestures. We present results on the acquisition of lexicons and language models from raw speech, text, and phonetic transcripts, and demonstrate that our algorithm compares very favorably to other reported results with respect to segmentation performance and statistical efficiency.
URI: http://koha.mediu.edu.my:8181/xmlui/handle/1721
Other Identifiers: AIM-1558
CBCL-129
http://hdl.handle.net/1721.1/7191
Appears in Collections:MIT Items

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