Classification of phonemes using modulation spectrogram based features for Gujarati language

In this paper, features extracted from modulation spectrogram are used to classify the phonemes in Gujarati language. Modulation spectrogram which is a 2-dimensional (i.e., 2-D) feature vector, is then reduced to a smaller feature dimension by using the proposed feature extraction method. Gujarati database was manually segmented in 31 phoneme classes. These phonemes are then classified using support vector machine (SVM) classifier. Classification accuracy of phoneme classification is 94.5 % as opposed to classification with the state-of-the-art feature set Mel frequency cepstral coefficients (MFCC), which yields 92.74 % classification accuracy. Classification accuracy for broad phoneme classes, viz., vowel, stops, nasals, semivowels, affricates and fricatives is also determined. Phoneme classification in their respective classes is 95.03 % correct with the proposed feature set. Fusion of MFCC with the proposed feature set is performing even better, giving phoneme classification accuracy of 95.7%. With the fusion of features phoneme classification in sonorant and obstruent classes is found to be 97.01 % accurate.