Exploring Universal Attribute Characterization of Spoken Languages for Spoken Language Recognition - PowerPoint PPT Presentation

Exploring universal attribute characterization of spoken languages for spoken language recognition l.jpg
Download
1 / 9

  • 362 Views
  • Uploaded on
  • Presentation posted in: Pets / Animals

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.

I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.

Download Presentation

Exploring Universal Attribute Characterization of Spoken Languages for Spoken Language Recognition

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript


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

  • Introduction

  • UAR-FrondEnd

  • VSM-BackEnd

  • Experiment


Introduction l.jpg

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.


System overview l.jpg

System Overview


Uar frondend l.jpg

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 l.jpg

VSM-BackEnd

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


Experiment l.jpg

Experiment

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


Experiment8 l.jpg

Experiment

  • 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


  • Login