This paper provides the classification of emotionally annotated speech of mentally impaired people. The main problem encountered in the classification task is the class-imbalance. This imbalance is due to the availability of large number of speech samples for the neutral speech compared to other emotional speech. Different sampling methodologies are explored at the back-end to handle this class-imbalance problem. Mel-frequency cepstral coefficients (MFCCs) features are considered at the front-end, eep neural networks (DNNs) and gradient boosted decision trees (GBDT) are investigated at the back-end as classifiers. The experimental results obtained from the EmotAsS dataset have shown higher classification accuracy and Unweighted Average Recall (UAR) scores over the baseline system.
DOI: 10.21437/SMM.2018-2
Cite as: V, V.V.R., Jain, P., Gurugubelli, K., Vuppala, A.K. (2018) Emotional Speech Classifier Systems: For Sensitive Assistance to support Disabled Individuals. Proc. Workshop on Speech, Music and Mind 2018, 6-10, DOI: 10.21437/SMM.2018-2.
@inproceedings{V2018,
author={Vishnu Vidyadhara Raju V and Priyam Jain and Krishna Gurugubelli and Anil Kumar Vuppala},
title={Emotional Speech Classifier Systems: For Sensitive Assistance to support Disabled Individuals},
year=2018,
booktitle={Proc. Workshop on Speech, Music and Mind 2018},
pages={6--10},
doi={10.21437/SMM.2018-2},
url={http://dx.doi.org/10.21437/SMM.2018-2}
}