Study on emotional speech features in Korean with its aplication to voice color conversion

2015 ◽

Author(s):

Hasrul Mohd Nazid ◽

Hariharan Muthusamy ◽

Vikneswaran Vijean ◽

Sazali Yaacob

Keyword(s):

Emotion Recognition ◽

Principal Component ◽

Feature Reduction ◽

Speech Emotion Recognition ◽

Emotional Speech ◽

Two Stage ◽

Linear Discriminant ◽

Speaker Independent ◽

Speech Features ◽

And Gender

In the recent years, researchers are focusing to improve the accuracy of speech emotion recognition. Generally, high emotion recognition accuracies were obtained for two-class emotion recognition, but multi-class emotion recognition is still a challenging task . The main aim of this work is to propose a two-stage feature reduction using Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) for improving the accuracy of the speech emotion recognition (ER) system. Short-term speech features were extracted from the emotional speech signals. Experiments were carried out using four different supervised classifi ers with two different emotional speech databases. From the experimental results, it can be inferred that the proposed method provides better accuracies of 87.48% for speaker dependent (SD) and gender dependent (GD) ER experiment, 85.15% for speaker independent (SI) ER experiment, and 87.09% for gender independent (GI) experiment.

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Study on Emotional Speech Features in Korean with Its Application to Voice Conversion

Affective Computing and Intelligent Interaction - Lecture Notes in Computer Science ◽

10.1007/11573548_44 ◽

2005 ◽

pp. 342-349 ◽

Cited By ~ 1

Author(s):

Sang-Jin Kim ◽

Kwang-Ki Kim ◽

Hyun Bae Han ◽

Minsoo Hahn

Keyword(s):

Voice Conversion ◽

Emotional Speech ◽

Speech Features

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Speech Features Extraction Techniques for Robust Emotional Speech Analysis/Recognition

Indian Journal of Science and Technology ◽

10.17485/ijst/2017/v10i3/110571 ◽

2017 ◽

Vol 10 (3) ◽

Cited By ~ 1

Author(s):

K. M. Shiva Prasad ◽

G. N. Kodanda Ramaiah ◽

M. B. Manjunatha

Keyword(s):

Features Extraction ◽

Speech Analysis ◽

Emotional Speech ◽

Extraction Techniques ◽

Speech Features

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Stress Level Detection and Evaluation from Phonation and PPG Signals Recorded in an Open-Air MRI Device

Applied Sciences ◽

10.3390/app112411748 ◽

2021 ◽

Vol 11 (24) ◽

pp. 11748

Author(s):

Jiří Přibil ◽

Anna Přibilová ◽

Ivan Frollo

Keyword(s):

Stress Level ◽

Gaussian Mixture Models ◽

Gaussian Mixture ◽

Emotional Speech ◽

Analysis And Evaluation ◽

Final Stress ◽

Speech Features ◽

Two Phases ◽

And Training ◽

Silent State

This paper deals with two modalities for stress detection and evaluation—vowel phonation speech signal and photo-plethysmography (PPG) signal. The main measurement is carried out in four phases representing different stress conditions for the tested person. The first and last phases are realized in laboratory conditions. The PPG and phonation signals are recorded inside the magnetic resonance imaging scanner working with a weak magnetic field up to 0.2 T in a silent state and/or with a running scan sequence during the middle two phases. From the recorded phonation signal, different speech features are determined for statistical analysis and evaluation by the Gaussian mixture models (GMM) classifier. A database of affective sounds and two databases of emotional speech were used for GMM creation and training. The second part of the developed method gives comparison of results obtained from the statistical description of the sensed PPG wave together with the determined heart rate and Oliva–Roztocil index values. The fusion of results obtained from both modalities gives the final stress level. The performed experiments confirm our working assumption that a fusion of both types of analysis is usable for this task—the final stress level values give better results than the speech or PPG signals alone.

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