Emotion Detection from Speech and Text
De Velasco Vázquez, Mikel
Justo Blanco, Raquel
Torres Barañano, María Inés
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IberSPEECH 2018 21-23 November 2018, Barcelona, Spain : 68-71 (2018)
The main goal of this work is to carry out automatic emo-tion detection from speech by using both acoustic and textualinformation. For doing that a set of audios were extracted froma TV show were different guests discuss about topics of currentinterest. The selected audios were transcribed and annotatedin terms of emotional status using a crowdsourcing platform.A 3-dimensional model was used to define an specific emo-tional status in order to pick up the nuances in what the speakeris expressing instead of being restricted to a predefined set ofdiscrete categories. Different sets of acoustic parameters wereconsidered to obtain the input vectors for a neural network. Torepresent each sequence of words, a models based on word em-beddings was used. Different deep learning architectures weretested providing promising results, although having a corpus ofa limited size.