Speech classification using SIFT features on spectrogram images

Abstract

Classification of speech is one of the most vital problems in speech processing. Although there have been many studies on the classification of speech, the results are still limited. Firstly, most of the speech classification approaches requiring input data have the same dimension. Secondly, all traditional methods must be trained before classifying speech signal and must be retrained when having more training data or new class. In this paper, we propose an approach for speech classification using Scale-invariant Feature Transform (SIFT) features on spectrogram images of speech signal combination with Local naïve Bayes nearest neighbor. The proposed approach allows using feature vectors to have different sizes. With this approach, the achieved classification results are satisfactory. They are 73, 96, 95, 97 %, and 97 % on the ISOLET, English Isolated Digits, Vietnamese Places, Vietnamese Digits, JVPD databases, respectively. Especially, in a subset of the TMW database, the accuracy is 100 %. In addition, in our proposed approach, non-retraining is needed for additional training data after the training phase. The experiment shows that the more features are added to the model, the more is the accuracy in performance.