| Paper: | MSP-P1.1 | ||
| Session: | Human Machine Interface; Signal Processing for Media Integration and Application | ||
| Time: | Friday, May 21, 09:30 - 11:30 | ||
| Presentation: | Poster | ||
| Topic: | Multimedia Signal Processing: Human-Machine Interface | ||
| Title: | IMPROVED FACE AND FEATURE FINDING FOR AUDIO-VISUAL SPEECH RECOGNITION IN VISUALLY CHALLENGING ENVIRONMENTS | ||
| Authors: | Jintao Jiang; University of California, Los Angeles | ||
| Gerasimos Potamianos; IBM T. J. Watson Research Center | |||
| Harriet J. Nock; IBM T. J. Watson Research Center | |||
| Giridharan Iyengar; IBM T. J. Watson Research Center | |||
| Chalapathy Neti; IBM T. J. Watson Research Center | |||
| Abstract: | Visual information in a speaker’s face is known to improve robustness of automatic speech recognizers. However, most studies in audio-visual ASR have focused on “visually clean” data to benefit ASR in noise. This paper is a follow up on a previous study that investigated audio-visual ASR in visually challenging environments. It focuses on visual speech front end processing, and it proposes an improved, appearance based face and feature detection algorithm that utilizes Gaussian mixture model classifiers. This method is shown to improve the accuracy of face and feature detection, and thus visual speech recognition, over our previously used baseline system. In turn, this translates to improved audio-visual ASR, resulting in a 10% relative reduction of the word-error-rate in noisy speech. | ||
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