Personalised Emotion Recognition Utilising Speech Signal and Linguistic Cues

2019 ◽  
Author(s):  
H. R. Ramya ◽  
Mahabaleswara Ram Bhatt
Keyword(s):  
Emotion Recognition ◽  
Speech Signal ◽  
Linguistic Cues

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.

Automatic emotion recognition of speech signal in Mandarin

2006 ◽  
Author(s):  
Sheng Zhang ◽  
P. C. Ching ◽  
Fanrang Kong
Keyword(s):  
Emotion Recognition ◽  
Speech Signal

Personalized Emotion Recognition Utilizing Speech Signal and Linguistic Clues

2021 ◽  
pp. 1397-1405
Author(s):  
A. V. Mohan Kumar ◽  
H. V. Chaitra ◽  
S. Shalini ◽  
D. Shruthi
Keyword(s):  
Emotion Recognition ◽  
Speech Signal

Binary tree SVM-based Emotion Recognition from Speech Signal

2013 ◽  
Vol 5 (1) ◽  
pp. 224-232
Author(s):  
Liqin Fu ◽  
Haiguang Zhai ◽  
Yongmei Zhang ◽  
Dan Yu
Keyword(s):  
Emotion Recognition ◽  
Speech Signal ◽  
Binary Tree

Emotion recognition from speech signal

2017 ◽  
Author(s):  
Esther Ramdinmawii ◽  
Abhijit Mohanta ◽  
Vinay Kumar Mittal
Keyword(s):  
Emotion Recognition ◽  
Speech Signal

EEMD-Based Speaker Emotional Analysis for Speech Signal

2011 ◽  
Vol 121-126 ◽  
pp. 815-819 ◽  
Author(s):  
Yu Qiang Qin ◽  
Xue Ying Zhang
Keyword(s):  
Emotion Recognition ◽  
Empirical Mode Decomposition ◽  
Speech Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
New Method ◽  
Speech Signals ◽  
Emotional Speech ◽  
Mode Decomposition ◽  
Two Parameters ◽  
Mode Mixing

Ensemble empirical mode decomposition(EEMD) is a newly developed method aimed at eliminating mode mixing present in the original empirical mode decomposition (EMD). To evaluate the performance of this new method, this paper investigates the effect of two parameters pertinent to EEMD: the emotional envelop and the number of emotional ensemble trials. At the same time, the proposed technique has been utilized for four kinds of emotional(angry、happy、sad and neutral) speech signals, and compute the number of each emotional ensemble trials. We obtain an emotional envelope by transforming the IMFe of emotional speech signals, and obtain a new method of emotion recognition according to different emotional envelop and emotional ensemble trials.

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