Efficient Deep Learning Network Implementation for NLP based Voice Modulation using Mel Frequency Cepstral Coefficient (MFCC)


Ishita Aggarwal , DIT University Dehradun; Maneesh K Singh, DIT University Dehradun; Dr. Sandeep Sharma, DIT University, Dehradun


Deep Neural Nets , Natural Language Processing , MFCC


Neural networks and deep learning currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing. In this Paper we propose a simple technique train the neural network for speech modulation. The data sets used in the training have used the technique of Mel frequency cepstral coefficient (MFCC) to extract features from speech and map the differences in them between different speakers to generate a modulation vector. We aim that the host voice when modulated into target voice use this network to learn these modulation from large-scale unlabeled data. The network would modulate the speech without the modulation vector after a certain amount of time students.

Other Details

Paper ID: NCILP009
Published in: Conference 1 : NCIL 2015
Publication Date: 16/10/2015
Page(s): 34-36

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