@conference {291, title = {Acoustic Emotion Recognition using Dynamic Bayesian Networks and Multi-Space Distributions}, booktitle = {10th Annual Conference of the Internacional Speech Communication Association (INTERSPEECH 2009)}, year = {2009}, month = {09/2009}, pages = {336-339}, publisher = {Internacional Speech Communication Association}, organization = {Internacional Speech Communication Association}, address = {Brighton, U.K.}, abstract = {

In this paper we describe the acoustic emotion recognition
system built at the Speech Technology Group of the Universidad
Politecnica de Madrid (Spain) to participate in the INTERSPEECH
2009 Emotion Challenge. Our proposal is based on
the use of a Dynamic Bayesian Network (DBN) to deal with
the temporal modelling of the emotional speech information.
The selected features (MFCC, F0, Energy and their variants) are
modelled as different streams, and the F0 related ones are integrated
under a Multi Space Distribution (MSD) framework, to
properly model its dual nature (voiced/unvoiced). Experimental
evaluation on the challenge test set, show a 67.06\% and 38.24\%
of unweighted recall for the 2 and 5-classes tasks respectively.
In the 2-class case, we achieve similar results compared with
the baseline, with 8.5 times less features. In the 5-class case, we
achieve a statistically significant 6.5\% relative improvement.

}, keywords = {automatic emotion recognition, dynamic bayesian networks, emotion challenge, multi-space probability distribution}, attachments = {https://geintra-uah.org/en/system/files/p46039.pdf}, author = {Roberto Barra-Chicote and Fernando Fernandez and Syaheerah L. Lutfi and Juan Manuel Lucas and Javier Macias-Guarasa and Juan Manuel Montero and San Segundo, Ruben and Jose Manuel Pardo} }