Speaker-independent visual speech recognition with the inception v3 model

Timothy Israel Santos, Andrew Abel, Nick Wilson, Yan Xu

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

12 Citations (Scopus)

Abstract

The natural process of understanding speech involves combining auditory and visual cues. CNN based lip reading systems have become very popular in recent years. However, many of these systems consider lipreading to be a black box problem, with limited detailed performance analysis. In this paper, we performed transfer learning by training the Inception v3 CNN model, which has pre-trained weights produced from IMAGENET, with the GRID corpus, delivering good speech recognition results, with 0.61 precision, 0.53 recall, and 0.51 F1-score. The lip reading model was able to automatically learn pertinent features, demonstrated using visualisation, and achieve speaker-independent results comparable to human lip readers on the GRID corpus. We also identify limitations that match those of humans, therefore limiting potential deep learning performance in real world situations.

Original languageEnglish
Title of host publicationProceedings of SLT 2021
Subtitle of host publicationIEEE Spoken Language Technology Workshop
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages613-620
Number of pages8
ISBN (Electronic)9781728170664
DOIs
Publication statusPublished - 2021
EventIEEE Workshop on Spoken Language Technology - Shenzhen, China
Duration: 19 Jan 202122 Jan 2021
http://2021.ieeeslt.org/

Publication series

NameIEEE Workshop on Spoken Language Technology
PublisherIEEE
ISSN (Print)2639-5479

Conference

ConferenceIEEE Workshop on Spoken Language Technology
Abbreviated titleSLT2021
Country/TerritoryChina
CityShenzhen
Period19/01/2122/01/21
Internet address

Keywords

  • deep learning
  • lip-reading
  • visual speech recognition

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