Classifying Emotional traits from Speech file using 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 15, 2019. | Revised Manuscript received on 20 November, 2019. | Manuscript published on December 10, 2019. | PP: 453469- | Volume-9 Issue-2, December 2019. | Retrieval Number: B6444129219/2019©BEIESP | DOI: 10.35940/ijitee.B6444.129219
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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: With the proliferated usage of speech as a voice command over various devices in existing times, it offers hands-free as well as comfortable experience for operating the current trends of devices. However, the devices are operated just on the basis of recognition of the speech commands and not its context. It is now well known that context factor plays a contributory role in mechanism an artificial intelligence in such devices where a comprehensive analytical modeling can be carried out. In the direction of extraction contextual information from speech, it is first necessary to recognize the speech in it logical form which can be disrupted because of various external causes. Apart from this, even if the recognition is carried out well than the next challenging part will be to extract the emotional factor within the speech. The existing review shows that research-based approaches are not sufficient enough to extract meaning full information which causes a greater degree of impediment in performing classification. Therefore, the proposed system introduces a novel and cost effective framework where machine learning is utilized in a very unique manner for performing this task. The paper discusses about an analytica approach which perform feature extraction followed by applying machine learning to show that proposed system offers faster response time with higher matching found during the testing operation phase.
Keywords: Speech, Classification, emotion, Machine Learning, SVM
Scope of the Article: Classification