Presentation 2014-12-16
Automatic Language Identification Based on Posterior Probability on Articulatory Classes
Takumi HIRATA, Kazuyuki TAKAGI,
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Abstract(in English) Extraction of features from input speech that are effective in distinguishing the language is a key issue for language identification system. We use posterior probabilities on articulatory classes as features for language identification. Posterior probability on each articulatory class is calculated by GMMs. Each GMM is trained with MFCC data of speech segments labeled with the phonemes or acoustic events that correspond to the articulatory class. The posterior probability values of the articulatory classes are concatenated to form an articulatory-feature-class-posterior-probability (AFCPP) vector at each analysis frame. These vectors are then quantized to yield VQ code sequence, which is used as the training data for a n-gram language model. Language identification is performed by selecting the n-gram model that yields the highest likelihood for the AFCPP vector sequence of the input utterance. Language identification experiment between Japanese and English by the present method showed identification rate of 97.1%.
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Keyword(in English) language identification / articulatory class / posterior probability / International Phonetic Alphabet
Paper # SP2014-125
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Conference Information
Committee SP
Conference Date 2014/12/8(1days)
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Registration To Speech (SP)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Automatic Language Identification Based on Posterior Probability on Articulatory Classes
Sub Title (in English)
Keyword(1) language identification
Keyword(2) articulatory class
Keyword(3) posterior probability
Keyword(4) International Phonetic Alphabet
1st Author's Name Takumi HIRATA
1st Author's Affiliation The University of Electro-Communications()
2nd Author's Name Kazuyuki TAKAGI
2nd Author's Affiliation The University of Electro-Communications
Date 2014-12-16
Paper # SP2014-125
Volume (vol) vol.114
Number (no) 365
Page pp.pp.-
#Pages 5
Date of Issue