Emotion Detection from Speech and Text

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.This work has been partially founded by bythe Spanish Government (TIN2014-54288-C4-4-R and TIN2017-85854-C4-3-R), and bythe European Commission H2020 SC1-PM15program under RIA 7 grant 69872