Machine Learning in Human Emotion Detection from the Speech

2021 ◽  
Author(s):  
Xiaoli Qiu ◽  
Wei Li ◽  
Yang Li ◽  
Hongmei Gu ◽  
Fei Song ◽  
...  
Keyword(s):  
Machine Learning ◽  
Emotion Recognition ◽  
Pulse Power ◽  
Speech Emotion Recognition ◽  
Learning Science ◽  
Emotion Detection ◽  
Classification Techniques ◽  
Human Emotion ◽  
Research Findings

The identification of speech emotions is amongst the most strenuous and fascinating fields of machine learning science. In this article, Chinese emotions are classified as a disruptive atmosphere that classifies several feelings into four major emotional organizations: pleasure, sorrow, resentment, and neutrality. A machine learning in human emotion detection (ML-HED) framework is proposed. The technology suggested removing prosodic and spectrum elements of an audio wave, such as a pulse, power, amplitude, Cepstrum melt frequency correlations, linearly fixed Cepstral, and identification with a template. In all, 87,75% of performers’ statements and 93% of women’s actors were given reliability. The research findings show that the revolutionary technology achieves greater precision by accurately interpreting the feelings, which contrasts with current speech emotion recognition approaches. Besides, the derived characteristics were contrasting with various classification techniques in this study for the comprehensive idea.

scholarly journals EEG-based human emotion recognition using k-NN machine learning

2019 ◽  
Author(s):  
A. A. Yusuf ◽  
S. K. Wijaya ◽  
P. Prajitno
Keyword(s):  
Machine Learning ◽  
Emotion Recognition ◽  
Human Emotion

scholarly journals An Appraisal on Speech and Emotion Recognition Technologies based on Machine Learning

2020 ◽  
Vol 8 (5) ◽  
pp. 2266-2276 ◽  
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Emotion Recognition ◽  
Speech Development ◽  
Speech Emotion Recognition ◽  
Part Of Speech ◽  
Classification Feature ◽  
The Way

In earlier days, people used speech as a means of communication or the way a listener is conveyed by voice or expression. But the idea of machine learning and various methods are necessary for the recognition of speech in the matter of interaction with machines. With a voice as a bio-metric through use and significance, speech has become an important part of speech development. In this article, we attempted to explain a variety of speech and emotion recognition techniques and comparisons between several methods based on existing algorithms and mostly speech-based methods. We have listed and distinguished speaking technologies that are focused on specifications, databases, classification, feature extraction, enhancement, segmentation and process of Speech Emotion recognition in this paper

scholarly journals Speech Based Emotion Recognition Using Machine Learning

2021 ◽  
Vol 9 (12) ◽  
pp. 2093-2095
Author(s):  
Vaibhav K. P.
Keyword(s):  
Machine Learning ◽  
Emotion Recognition ◽  
Research Topic ◽  
Speech Emotion Recognition ◽  
Lexical Analysis ◽  
Main Motive

Abstract: Speech emotion recognition is a trending research topic these days, with its main motive to improve the humanmachine interaction. At present, most of the work in this area utilizes extraction of discriminatory features for the purpose of classification of emotions into various categories. Most of the present work involves the utterance of words which is used for lexical analysis for emotion recognition. In our project, a technique is utilized for classifying emotions into Angry',' Calm', 'Fearful', 'Happy', and 'Sad' categories.

scholarly journals Databases, Features and Classification Techniques for Speech Emotion Recognition

2020 ◽  
Vol 9 (6) ◽  
pp. 185-190
Keyword(s):  
Emotion Recognition ◽  
Research Area ◽  
Research Field ◽  
Speech Emotion Recognition ◽  
Emotional Speech ◽  
Human Machine Interaction ◽  
Classification Techniques ◽  
Classification Feature ◽  
Physical Gestures ◽  
Machine Interaction

Emotion recognition is a rapidly growing research field. Emotions can be effectively expressed through speech and can provide insight about speaker’s intentions. Although, humans can easily interpret emotions through speech, physical gestures, and eye movement but to train a machine to do the same with similar preciseness is quite a challenging task. SER systems can improve human-machine interaction when used with automatic speech recognition, as emotions have the tendency to change the semantics of a sentence. Many researchers have contributed their extremely impressive work in this research area, leading to development of numerous classification, feature selection, feature extraction and emotional speech databases. This paper reviews recent accomplishments in the area of speech emotion recognition. It also present a detailed review of various types of emotional speech databases, and different classification techniques which can be used individually or in combination and a brief description of various speech features for emotion recognition.

Human Emotion Detection using Machine Learning Techniques

2020 ◽  
Author(s):  
Punidha Angusamy ◽  
Inba S ◽  
Pavithra K.S ◽  
Ameer Shathali M ◽  
Athiparasakthi M
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Emotion Detection ◽  
Human Emotion ◽  
Learning Techniques

scholarly journals Automatic Speech Emotion Recognition Using Machine Learning

2020 ◽  
Author(s):  
Leila Kerkeni ◽  
Youssef Serrestou ◽  
Mohamed Mbarki ◽  
Kosai Raoof ◽  
Mohamed Ali Mahjoub ◽  
...  
Keyword(s):  
Machine Learning ◽  
Emotion Recognition ◽  
Speech Emotion Recognition
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