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Emotional Speech Classifier Systems: For Sensitive Assistance to support Disabled IndividualsAuthors: Vishnu Vidyadhara Raju V,Priyam Jain,Krishna Gurugubelli,Anil Kumar Vuppala Conference: SMM-2018 Satellite Workshop as part of INTERSPEECH-2018 (INTERSPEECH-2018 2018) Location IIIT Hyderabad, Hyderabad, India Date: 2018-09-01 Report no: IIIT/TR/2018/20 AbstractThis 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, deep 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. Full paper: pdf Centre for Language Technologies Research Centre |
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