Speech Classification using Logical ART Deep Mechanism of Machine Learning
Pooja Nayak S1, S G Hiremath2, Arun Biradar3

1Pooja Nayak S*, Research Scholar, EWIT, Bangalore, India.
2S G Hiremath, Professor and Head, Department of ECE, EWIT, Bangalore, India
3Arun Birader, Professor, CMR University, Bangalore, India.

Manuscript received on November 12, 2019. | Revised Manuscript received on 21 November, 2019. | Manuscript published on December 10, 2019. | PP: 2605-2611 | Volume-9 Issue-2, December 2019. | Retrieval Number: B7239129219/2019©BEIESP | DOI: 10.35940/ijitee.B7239.129219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Apart from this there are many domains including medical, voice synthesis, hate speech classification and other custom applications where classification of speech plays an important role. The conventional techniques of speech processing and classification works on a small data set also provide lower accuracy of the classification. This paper introduces a learning model using neural network (NN) for the large dataset machine training and classification using critical feature analysis for the pattern of speech spectrogram and waveforms. The performance evaluation of the proposed training model for the speech classification is validated on a single CPU and found to achieve (12-82) % of accuracy in just 5-epochs and also continuously decreases the loss at successive iteration of the epochs. This method provides learning model framework for the speech processing and classification for a very large dataset. 
Keywords: Speech, Classification, Machine learning, Large speech data.
Scope of the Article: Classification