DSCC features for Hindi vowel classification
Shipra Shipra, Mahesh Chandra · 2017
MFCC is a popular feature extraction technique for speech recognition applications. Various researchers have worked with variations of MFCC features. Delta Spectral Cepstral Coefficient (DSCC) is a new feature extraction technique for speech recognition. It is similar in its approach to MFCC features and gives much better recognition accuracy compared to MFCC features in noisy environments and dynamically changing environments. Hence, DSCC features are more suitable for real life speech recognition application. Here we have worked with DSCC and MFCC features for Hindi vowel classification task. Hidden Markov Model is used as the classifier. It has been observed that DSCC features improved the classification efficiency by 6.16% and 5.186% for car noise at −5 dB and 10 dB respectively.