Combining Global and Local Classifiers for Lipreading

摘要

Lipreading has become a hot research topic in recent years since the visual in-formation extracted from the lip movement has been shown to improve the performance of automatic speech recognition (ASR) system especially under noisy environments [l]-[3], [5]. There are two important issues related to lipreading: 1) how to extract the most efficient features from lip image sequences, 2) how to build lipreading models. This paper mainly focuses on how to choose more efficient features for lipreading.