Comparison of Image Transform-Based Features for Visual Speech Recognition in Clean and Corrupted Videos

Rowan Seymour, Darryl Stewart, Ming Ji

Research output: Contribution to journalArticle

33 Citations (Scopus)
310 Downloads (Pure)

Abstract

We present results of a study into the performance of a variety of different image transform-based feature types for speaker-independent visual speech recognition of isolated digits. This includes the first reported use of features extracted using a discrete curvelet transform. The study will show a comparison of some methods for selecting features of each feature type and show the relative benefits of both static and dynamic visual features. The performance of the features will be tested on both clean video data and also video data corrupted in a variety of ways to assess each feature type's robustness to potential real-world conditions. One of the test conditions involves a novel form of video corruption we call jitter which simulates camera and/or head movement during recording.
Original languageEnglish
Article number810362
Pages (from-to)1-9
Number of pages9
JournalEURASIP Journal on Image and Video Processing
Volume2008
DOIs
Publication statusPublished - Jan 2008

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Signal Processing
  • Information Systems

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  • Impacts

    Applications of Novel Speech and Audio-Visual Processing Research

    Ming Ji (Participant), Ramji Srinivasan (Participant), Daniel Crookes (Participant), Darryl Stewart (Participant), Niall McLaughlin (Participant) & Roger Woods (Participant)

    Impact: Economic Impact, Health Impact

    Cite this