Hindi Vowel Classification using QCN-PNCC Features

This paper present a novel hybridized QCN-PNCC features. These features are obtained by processing Power Normalized Cepstral Coefficients (PNCC) with Quantile based Dynamic Cepstral Normalization Technique (QCN). The robustness of the QCN-PNCC features is compared with PNCC features for the task of Hindi Vowel classification with HMM classifier for Context-Dependent and Context- Independent cases in clean as well as in noisy environment. It is observed that the recognition accuracy of QCN-PNCC features with Hidden Markov Model (HMM) as classifier exhibit an improvement of approximately 8% as compared to PNCC features for Hindi vowel classification task.