Continuous automatic speech recognition by lipreading
Continuous automatic speech recognition by lipreading
Extraction of Visual Features for Lipreading
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
A hybrid visual feature extraction method for audio-visual speech recognition
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Robust Audio-Visual Speech Recognition Based on Late Integration
IEEE Transactions on Multimedia
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In this paper, relevant features for a visual speech recognition system are selected using Minimum Redundancy Maximum Relevance (mRMR) method. Feature vectors, with varying number of relevant attributes, are tested to determine the most optimal feature set. It is observed that a few relevant attributes perform considerably well compared to the complete feature vector. Using this feature set as a base vector, concatenation of features is done frame-wise to build n-gram models, so as to capture the temporal behaviour of speech. It is observed that the base mRMR feature vector is able to outperform the dynamic model of n-grams. This feature vector, consisting of only significant attributes, requires less processing time due to its small size. Storage requirements are also considerably reduced.