@inproceedings{1cb808d80eed4e7fa8ceaa7d9f467391,
title = "An exemplar-based hidden markov model with discriminative visual features for lipreading",
abstract = "In this paper, we address an exemplar-based hidden markov model (HMM) that represents the lip motion activity using visual cues for lipreading. The discriminative visual features including the geometric shape parameters and contour-constrained spatial histogram are selected for representing each lip frame. Then, a set of exemplars associated with the HMM is learned jointly to serve as a typical representation of a speech utterance. Based on these exemplars, the high-dimensional frame features are transformed to the lower dimensional ones, namely Frame to Exemplar Distance (FED) vector. Subsequently, a continuous HMM is trained using such FED vector sequences for learning and recognition. Experiments show the promising results.",
keywords = "Exemplar, FED vector, HMM, Lipreading",
author = "Xin Liu and CHEUNG, {Yiu Ming}",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 10th International Conference on Computational Intelligence and Security, CIS 2014 ; Conference date: 15-11-2014 Through 16-11-2014",
year = "2015",
month = jan,
day = "20",
doi = "10.1109/CIS.2014.74",
language = "English",
series = "Proceedings - 2014 10th International Conference on Computational Intelligence and Security, CIS 2014",
publisher = "IEEE",
pages = "90--93",
booktitle = "Proceedings - 2014 10th International Conference on Computational Intelligence and Security, CIS 2014",
address = "United States",
}