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

International Journal For Scientific Research & Development :: IJSRD


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.

A. Deep Learning:

Deep learning (deep machine learning, or deep structured learning, or hierarchical learning, or sometimes DL) is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data architectures, with complex structures or otherwise, composed of multiple non-linear transformations.
Deep learning is part of a broader family of machine learning methods based on learning representations of data. An
observation (e.g., an image) can be………………. Click Here


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