%X The growing demand to develop an automatic emotion recognition system for the Human-Computer Interaction field had pushed some research in speech emotion detection. %I European Language Resources association %S Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL) %T Speech-Emotion Detection in an Indonesian Movie The multiclass classification resulted in 64.66% of precision, 66.79% of recall, and 64.83% of F1-score. The best accuracies given by one-vs-rest scenario for each emotion class with speech-transcript pairs using hybrid of non-temporal and embedding approach are 1) happiness: 76.31% 2) sadness: 86.46% 3) anger: 82.14% and 4) neutral: 68.51%. This study used Artificial Neural Network (ANN), Recurrent Neural Network (RNN) with Long Short Term Memory (LSTM) variation, word embedding, and also the hybrid of three to perform the classification task. statistical and temporal/sequence representations. There are two different speech data representations used in this study i.e. This study proposed several approaches to detect speech-emotion in the dialogs of an Indonesian movie by classifying them into 4 different emotion classes i.e. Another issue is the lack of standard corpus for this research area in Bahasa Indonesia. Although it is growing, there is still little research about automatic speech emotion detection in Bahasa Indonesia. The growing demand to develop an automatic emotion recognition system for the Human-Computer Interaction field had pushed some research in speech emotion detection. Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL) Speech-Emotion Detection in an Indonesian Movie Cite (Informal): Speech-Emotion Detection in an Indonesian Movie (Fahmi et al., SLTU 2020) Copy Citation: BibTeX Markdown MODS XML Endnote More options… PDF: = "Speech-Emotion Detection in an of F1-score.", In Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL), pages 185–193, Marseille, France. Speech-Emotion Detection in an Indonesian Movie. Anthology ID: 2020.sltu-1.26 Volume: Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL) Month: May Year: 2020 Address: Marseille, France Venue: SLTU SIG: Publisher: European Language Resources association Note: Pages: 185–193 Language: English URL: DOI: Bibkey: fahmi-etal-2020-speech Cite (ACL): Fahmi Fahmi, Meganingrum Arista Jiwanggi, and Mirna Adriani. Abstract The growing demand to develop an automatic emotion recognition system for the Human-Computer Interaction field had pushed some research in speech emotion detection.
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