The main goal of this work is to carry out automatic emotion detection from speech by using both acoustic and textual information. For doing that a set of audios were extracted from a TV show were different guests discuss about topics of current interest. The selected audios were transcribed and annotated in terms of emotional status using a crowdsourcing platform. A 3-dimensional model was used to define an specific emotional status in order to pick up the nuances in what the speaker is expressing instead of being restricted to a predefined set of discrete categories. Different sets of acoustic parameters were considered to obtain the input vectors for a neural network. To represent each sequence of words, a models based on word embeddings was used. Different deep learning architectures were tested providing promising results, although having a corpus of a limited size.
Cyberbullying is the use of digital media to harass a person or group of people, through personal attacks, disclosure of confidential or false information, among other means. That is to say, it is considered cyberbullying, or cyber-aggression to everything that is done through electronic communication devices with the intended purpose of harming or attacking a person or a group. In this paper we present a starting project to prevent cyberbullying between kids and teenagers. The idea is to create a prevention system. A system which is installed in the mobile of a kid and, if a harassment is detected, some advice is given to the child. In case of serious or repeated behavior the parents are alerted. The focus of this paper is to describe the characteristics of the database to be used to train the system
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