Paper Title
SPEECH EMOTION RECOGNITION WITH DEEP LEARNING USING FUSION
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Registration ID: IJNRD_200047
Published ID: IJNRD2306443
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Abstract
Since a long time ago, emotion identification from the voice signal has been a research issue in applications involving human-machine interfaces. Many techniques have been developed to extract the emotions from the speech stream. This paper reviews speech emotion recognition based on earlier technologies that employ several classifiers for emotion recognition. The classifiers are used to distinguish between many emotions, including neutral, happiness, sadness, and angry. Based on retrieved features, classification performance is achieved. In a two-stage process, feature extraction and a classification engine, . First, two sets of features are checked: the first is a 42- dimensional vector of audio features that contains 39 coefficients of the Mel Frequency Cepstral Coefficients (MFCC), the Zero Crossing Rate (ZCR), the Harmonic to Noise Rate (HNR), and the Teager Energy Operator (TEO). And for the second, we suggest using the Auto-Encoder approach to choose relevant parameters from the parameters that had previously been extracted. Second, as a classifier approach, we use Support Vector Machines (SVM) and Random Forest (RF). this paper suggests a speech signalbased emotion recognition system. .
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How To Cite (APA)
Singathi Sreeja, Nagunoori Krishna chetan, Kokkula Sandhya, Nallavelli Manohari, & Thurpati sammaiah (June-2023). SPEECH EMOTION RECOGNITION WITH DEEP LEARNING USING FUSION. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(6), e326-e336. https://ijnrd.org/papers/IJNRD2306443.pdf
Issue
Volume 8 Issue 6, June-2023
Pages : e326-e336
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Paper Reg. ID: IJNRD_200047
Published Paper Id: IJNRD2306443
Research Area: Engineering
Author Type: Indian Author
Country: karimnagar, Telangana, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2306443.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2306443
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