Title :
Lip feature selection based on BPSO and SVM
Author_Institution :
Sch. of Inf. Eng., Hebei Univ. of Technol., Tianjin, China
Abstract :
In speech synthesis system driven by visual speech, many irrelevant and redundant features will lessen the lipreading recognition result. So it is important to select lip features with stronger discriminate performance. Feature selection algorithm based on binary particle swarm optimization (BPSO) and support vector machines (SVM) is used to select the “optimal” lip feature subset. Feature subset was generated randomly firstly, and then BPSO algorithms searched the feature space guided by the result of SVMs´ 10-fold crossover validation. After numbers of iteration, the best fitness feature subset was selected out as the vector of lip feature. Hidden Markov Model (HMM) with 4 states and 16 Gaussian mixture components is designed as a recognizer. Comparing with feature fusion based on concatenating, Experiment results in a small database for speaker-dependent case showed that the recognition rates with feature selection based on BPSO and SVM are improved by as much as 3.89%.
Keywords :
Gaussian processes; feature extraction; hidden Markov models; image fusion; particle swarm optimisation; speech recognition; speech synthesis; support vector machines; BPSO; Gaussian mixture; SVM; binary particle swarm optimization; feature fusion; hidden Markov model; lip feature selection; lip feature subset; lipreading recognition; speech synthesis system; support vector machine; visual speech; Discrete cosine transforms; Feature extraction; Hidden Markov models; Speech; Speech recognition; Support vector machines; Visualization; binary particle swarm optimization; feature selection; hidden Markov model; normalized DCT coefficients; normalized geometrical feature; support vector machines;
Conference_Titel :
Electronic Measurement & Instruments (ICEMI), 2011 10th International Conference on
Conference_Location :
Chengdu
Print_ISBN :
978-1-4244-8158-3
DOI :
10.1109/ICEMI.2011.6037854