scholarly journals Machine Learning Based Emotion Recognition using Speech Signal

2019 ◽  
Vol 9 (1S5) ◽  
pp. 295-301
Keyword(s):  
Machine Learning ◽  
Literature Review ◽  
Emotion Recognition ◽  
Speech Signal ◽  
Contemporary Literature ◽  
Speech Analysis ◽  
Speech Emotion Recognition ◽  
Analysis Methods ◽  
Computer Aided

The challenging module in CAS (computer-aided services) has recognized the emotion from the signals of speech. In SER (speech emotion recognition), several schemes have used for extracting emotions from the signals, comprising various classification & speech analysis methods. This manuscript represents an outline of methods & explores some contemporary literature where the existing models have used for emotion recognition based on speech. This literature review presents contributions that made towards emotion recognition of speech and extracted the features for determining emotions.

Speech Emotional Features Extraction Based on Electroglottograph

2013 ◽  
Vol 25 (12) ◽  
pp. 3294-3317 ◽  
Author(s):  
Lijiang Chen ◽  
Xia Mao ◽  
Pengfei Wei ◽  
Angelo Compare
Keyword(s):  
Emotion Recognition ◽  
Speech Signal ◽  
Vocal Tract ◽  
Vocal Folds ◽  
Distribution Coefficients ◽  
Speech Emotion Recognition ◽  
Support Vector ◽  
Power Law Distribution ◽  
Transform Coefficients ◽  
Better Than

This study proposes two classes of speech emotional features extracted from electroglottography (EGG) and speech signal. The power-law distribution coefficients (PLDC) of voiced segments duration, pitch rise duration, and pitch down duration are obtained to reflect the information of vocal folds excitation. The real discrete cosine transform coefficients of the normalized spectrum of EGG and speech signal are calculated to reflect the information of vocal tract modulation. Two experiments are carried out. One is of proposed features and traditional features based on sequential forward floating search and sequential backward floating search. The other is the comparative emotion recognition based on support vector machine. The results show that proposed features are better than those commonly used in the case of speaker-independent and content-independent speech emotion recognition.

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 Random Deep Belief Networks for Recognizing Emotions from Speech Signals

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Guihua Wen ◽  
Huihui Li ◽  
Jubing Huang ◽  
Danyang Li ◽  
Eryang Xun
Keyword(s):  
Emotion Recognition ◽  
Speech Signal ◽  
Majority Voting ◽  
Speech Emotion Recognition ◽  
Speech Signals ◽  
Belief Networks ◽  
Deep Belief Networks ◽  
Emotion Label ◽  
The Rich ◽  
Emotion Labels

Now the human emotions can be recognized from speech signals using machine learning methods; however, they are challenged by the lower recognition accuracies in real applications due to lack of the rich representation ability. Deep belief networks (DBN) can automatically discover the multiple levels of representations in speech signals. To make full of its advantages, this paper presents an ensemble of random deep belief networks (RDBN) method for speech emotion recognition. It firstly extracts the low level features of the input speech signal and then applies them to construct lots of random subspaces. Each random subspace is then provided for DBN to yield the higher level features as the input of the classifier to output an emotion label. All outputted emotion labels are then fused through the majority voting to decide the final emotion label for the input speech signal. The conducted experimental results on benchmark speech emotion databases show that RDBN has better accuracy than the compared methods for speech emotion recognition.

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