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 l.jpg

Exploring Universal Attribute Characterization of Spoken Languages for Spoken Language Recognition


Outline l.jpg
Outline Languages for Spoken Language Recognition

  • Introduction

  • UAR-FrondEnd

  • VSM-BackEnd

  • Experiment


Introduction l.jpg
Introduction Languages for Spoken Language Recognition

  • 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.


System overview l.jpg
System Overview Languages for Spoken Language Recognition


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UAR-FrondEnd Languages for Spoken Language Recognition

  • 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.


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VSM-BackEnd Languages for Spoken Language Recognition

  • Each transcription is converted into a vector-based representation by applying LSA.


Experiment l.jpg
Experiment Languages for Spoken Language Recognition

  • The OGI-TS corpus is used to train the articulatory recognizer. This corpus has phonetic transcriptions for six language.


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Experiment Languages for Spoken Language Recognition

  • CallFriend corpus is used for training the back-end language models.

  • Test are carried out on the NIST 2003 spoken language evaluation material.


Experiment9 l.jpg
Experiment Languages for Spoken Language Recognition