%A Quang Trung Nguyen
%A The Duy Bui
%A Thi Chau Ma
%T An Image based Approach for Speech Perception
%X Classification of speech signal is one of the most vital problems in speech perception and spoken word recognition. Although, there have been many studies on the classification of speech signals but the results are still limited. In this paper, we propose an image based approach for speech signal classification based on the combination of Local Naïve Bayes Nearest Neighbor (LNBNN) and Scale-invariant Feature Transform (SIFT) features. The proposed approach allows training feature vectors to have different sizes and no re-training is needed for additional training data after training phase. With this approach, achieved classification results are very satisfactory. They are 72.8, 100 and 95.0 on the ISOLET, Digits and Places databases, respectively.
%K Bayes methods;signal classification;speech recognition;transforms;vectors;ISOLET;LNBNN;SIFT features;digits;feature vectors;image based approach;local naive Bayes nearest neighbor;places databases;scale-invariant feature transform;speech perception;speech signal classification;spoken word recognition;training data;training phase;Databases;Feature extraction;Hidden Markov models;Mel frequency cepstral coefficient;Speech;Speech recognition;Training;lnbnn;sift;speech classification;speech perception
%P 208-213
%D 2015
%C Ho Chi Minh city, Vietnam
%L SisLab1464