exploring universal attribute characterization of spoken languages for spoken language recognition
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Exploring Universal Attribute Characterization of Spoken Languages for Spoken Language Recognition. Outline. Introduction UAR-FrondEnd VSM-BackEnd Experiment. Introduction. Here we focus on the token-based.

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exploring universal attribute characterization of spoken languages for spoken language recognition

Exploring Universal Attribute Characterization of Spoken Languages for Spoken Language Recognition

outline
Outline
  • Introduction
  • UAR-FrondEnd
  • VSM-BackEnd
  • Experiment
introduction
Introduction
  • Here we focus on the token-based.
  • Propse an alternative universal acoustic characterization of spoken languages based on acoustic phonetic feature.
  • The advantage of using attribute-based unit is they can be define universally across all language.
uar frondend
UAR-FrondEnd
  • The frond-end processing module tokenize all spoken utterances into sequences of speech unit using a universal attribute recognizer.
  • Two phoneme-to-attribute table are created that are phoneme-to-manner and phoneme-to-place.
vsm backend
VSM-BackEnd
  • Each transcription is converted into a vector-based representation by applying LSA.
experiment
Experiment
  • The OGI-TS corpus is used to train the articulatory recognizer. This corpus has phonetic transcriptions for six language.
experiment8
Experiment
  • CallFriend corpus is used for training the back-end language models.
  • Test are carried out on the NIST 2003 spoken language evaluation material.
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