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dc.contributor.authorChien, Jen-Tzungen_US
dc.date.accessioned2015-12-02T02:59:06Z-
dc.date.available2015-12-02T02:59:06Z-
dc.date.issued2015-08-01en_US
dc.identifier.issn2329-9290en_US
dc.identifier.urihttp://dx.doi.org/10.1109/TASLP.2015.2428632en_US
dc.identifier.urihttp://hdl.handle.net/11536/127849-
dc.description.abstractProbabilistic models are often viewed as insufficiently expressive because of strong limitation and assumption on the probabilistic distribution and the fixed model complexity. Bayesian nonparametric learning pursues an expressive probabilistic representation based on the nonparametric prior and posterior distributions with less assumption-laden approach to inference. This paper presents a hierarchical Pitman-Yor-Dirichlet (HPYD) process as the nonparametric priors to infer the predictive probabilities of the smoothed n-grams with the integrated topic information. A metaphor of hierarchical Chinese restaurant process is proposed to infer the HPYD language model (HPYD-LM) via Gibbs sampling. This process is equivalent to implement the hierarchical Dirichlet process-latent Dirichlet allocation (HDP-LDA) with the twisted hierarchical Pitman-Yor LM (HPY-LM) as base measures. Accordingly, we produce the power-law distributions and extract the semantic topics to reflect the properties of natural language in the estimated HPYD-LM. The superiority of HPYD-LM to HPY-LM and other language models is demonstrated by the experiments on model perplexity and speech recognition.en_US
dc.language.isoen_USen_US
dc.subjectBayesian nonparametricsen_US
dc.subjectlanguage modelen_US
dc.subjectspeech recognitionen_US
dc.subjecttopic modelen_US
dc.subjectunsupervised learningen_US
dc.titleHierarchical Pitman-Yor-Dirichlet Language Modelen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/TASLP.2015.2428632en_US
dc.identifier.journalIEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSINGen_US
dc.citation.volume23en_US
dc.citation.spage1259en_US
dc.citation.epage1272en_US
dc.contributor.department電機資訊學士班zh_TW
dc.contributor.departmentUndergraduate Honors Program of Electrical Engineering and Computer Scienceen_US
dc.identifier.wosnumberWOS:000356006200002en_US
dc.citation.woscount0en_US
Appears in Collections:Articles