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- W4313892499 abstract "Recognising emotions from a conversation is a paramount task for a machine to understand the full context of the conversation. However, recognising emotions is not an easy task for a machine that cannot understand social context. This research aims to explore several conventional machine learning methods in emotion recognition modelling, such as Na¨ıve Bayes, K-Nearest Neigh- bour, Decision Trees and Random Forest. Moreover, this research also presents the exploration of deep learning architecture such as Vanilla Deep Neural Networks, Convolutional Neural Networks and Long-Short Term Memory. Moreover, five Mel-Frequency Cepstral Coefficients numbers are also explored (32, 40, 48, 64, 128) with two sampling techniques (Fast Fourier Transform and Kaiser Fast). The results demonstrate that the best training accuracy is achieved by several algorithms such as KNN, ANN and LSTM (100%). Moreover, the model trained with ANN (73%) achieves the best training accuracy." @default.
- W4313892499 created "2023-01-10" @default.
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- W4313892499 date "2023-01-01" @default.
- W4313892499 modified "2023-09-29" @default.
- W4313892499 title "Exploring deep learning algorithm to model emotions recognition from speech" @default.
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- W4313892499 doi "https://doi.org/10.1016/j.procs.2022.12.187" @default.
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