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Hindi Vowel Classification using QCN-PNCC Features

Affiliations

  • Electronics and Communication Engineering Department, BIT Mesra, Near Patna Airport, Patna - 800014, Bihar, India
  • Electronics and Communication Engineering Department, BIT Mesra, Ranchi - 835215, Jharkhand, India

Abstract


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.

Keywords

Power normalized Cepstral Coefficient (PNCC), QCN, QCN-PNCC, Speech Recognition.

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