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World Journal of Engineering Research and Technology

( An ISO 9001:2015 Certified International Journal )

An International Peer Reviewed Journal for Engineering Research and Technology

An Official Publication of Society for Advance Healthcare Research (Reg. No. : 01/01/01/31674/16)

ISSN 2454-695X

Impact Factor : 7.029

ICV : 79.45

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Abstract

EMOTION SPEECH RECOGNITION SYSTEM FOR ISOLATED WORDS

Dhanashri Rachetti*, Shital Sukre, Asmita Gajarishi, Sagar Shirke and Ritesh Thakur

ABSTRACT

Recognition of emotions from speech is one of the most important sub domains in the field of affective computing. Sometimes, a person speaks the sentence while stay in some emotion which makes the tone of speech changes the meaning of the sentence completely. Speech signal consist not only the words and meaning but also it consist of the emotions. The emotion expressed by speech is one of the major influencing factors for the low recognition accuracy achieved during the development of speech based systems. When it comes to human speech emotions affects the tone and the speaking style of the person. The research in this area is needed to overcome these problems of emotion recognition from speech. However the problem is usually deals with the following basic emotion categories: Happy, Sad, Angry, Afraid, Surprise, Neutral. From the literature survey for the proposed study, it is observed that there is no proper emotional speech corpus in any of the Indian languages for carrying out the research on emotional speech processing. No any standard emotional speech database as well as the real life emotional speech database is available in the context of Indian languages. It is also observed from the literature that excitation source information is not thoroughly investigated for the purpose of emotion recognition task. Most of the researchers have used frame-wise spectral features extracted from entire utterance for speech emotion classification. Most of the existing emotionrecognition systems are developed using only gross prosodic features extracted from the entire utterances.

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