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Speech Emotion Detection Using Mel-Frequency Cepstral Coefficient and Hidden Markov Model | IEEE Conference Publication | IEEE Xplore

Speech Emotion Detection Using Mel-Frequency Cepstral Coefficient and Hidden Markov Model


Abstract:

Emotion is one of the human ways to interact with each other, which can be expressed using a speech. An emotion in one of the seven states of happy, angry, sad, calm, sca...Show More

Abstract:

Emotion is one of the human ways to interact with each other, which can be expressed using a speech. An emotion in one of the seven states of happy, angry, sad, calm, scared, shocked, and disgust can be identified through its wave speech signal. Development of speech recognition is a kind of technology that rapidly growing to help human-machine interaction, at the moment the most used method is the Hidden Markov Model (HMM). In this paper, a Mel-Frequency Cepstral Coefficient (MFCC), which is commonly used to generate certain coefficients, is exploited as a determinant parameter for HMM to classify those seven emotions. Evaluation for a dataset of 240 utterances shows that the developed model gives quite high accuracy of 81.65%.
Date of Conference: 10-11 December 2020
Date Added to IEEE Xplore: 13 January 2021
ISBN Information:
Conference Location: Yogyakarta, Indonesia

I. Introduction

The intensity of human-machine cooperation that routinely makes the need for systems that can understand humans are increasing. One example of a form of human-machine or machine-human interaction is speech. One aspect that can be observed from someone's speech is emotions, in the real world interaction among humans is greatly influenced by the way the pronunciation of words, otherwise then the essential aspects that exist in the speech can be lost or even lead to misunderstanding.

References

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