Procesamiento del Lenguaje Natural, Revista nº 61, septiembre de 2018, pp. 83-89 recibido 04-04-2018 revisado 29-04-2018 aceptado 04-05-2018 Bi-modal annoyance level detection from speech and text Detecci´ondel nivel de enfado mediante un sistema bi-modal basado en habla y texto Raquel Justo, Jon Irastorza, Saioa P´erez,M. In´esTorres Universidad del Pa´ısVasco UPV/EHU. Sarriena s/n. 48940 Leioa. Spain fraquel.justo,
[email protected] Abstract: The main goal of this work is the identification of emotional hints from speech. Machine learning researchers have analysed sets of acoustic parameters as potential cues for the identification of discrete emotional categories or, alternatively, of the dimensions of emotions. However, the semantic information gathered in the text message associated to its utterance can also provide valuable information that can be helpful for emotion detection. In this work this information is included within the acoustic information leading to a better system performance. Moreover, it is noticeable the use of a corpus that include spontaneous emotions gathered in a realistic environment. It is well known that emotion expression depends not only on cultural factors but also on the individual and on the specific situation. Thus, the conclusions extracted from the present work can be more easily extrapolated to a real system than those obtained from a classical corpus with simulated emotions. Keywords: speech processing, semantic information, emotion detection on speech, annoyance tracking, machine learning Resumen: El principal objetivo de este trabajo es la detecci´onde cambios emo- cionales a partir del habla. Diferentes trabajos basados en aprendizaje autom´atico han analizado conjuntos de par´ametrosac´usticoscomo potenciales indicadores en la identificaci´onde categor´ıasemocionales discretas o en la identificaci´ondimensional de las emociones.