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Speech emotion recognition exploiting acoustic and linguistic information sources (2005)
| Venue: | Proc. SPECOM |
| Citations: | 2 - 1 self |
BibTeX
@INPROCEEDINGS{Rigoll05speechemotion,
author = {Gerhard Rigoll and Ronald Müller and Björn Schuller},
title = {Speech emotion recognition exploiting acoustic and linguistic information sources},
booktitle = {Proc. SPECOM},
year = {2005},
pages = {61--67}
}
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Abstract
A variety of approaches towards Speech Emotion Recognition were presented since the research activities started in the late last decade. Today we are all aware of the great importance of emotional aspects as the next step towards more natural human-machine interaction. Yet, a growing number of isolated individual groups contribute to the general advances in the exciting landscape we see today. However, there is still a strong need for international cross-fertilization on the one hand in view of signal pre-processing, feature selection, and classification methods, and furthermore comparability with respect to emotion classes, and databases. As first commercial products arise today, the research community is challenged by more realistic out-of-the-lab conditions. This leads to the next level of Speech Emotion Recognition aiming among others at a robust recognition, independent of the speaker, backgroundnoise, and to a certain extent even of the spoken language by reasonable integration of acoustic, linguistic, and context information, as well as spotting for emotions out of a stream. In this article we like to give an overview of the existing pieces of the puzzle, introduce novel impulses, and aim to investigate future directions in this young discipline. 1.
Keyphrases
speech emotion recognition linguistic information source first commercial product young discipline next level signal pre-processing general advance international cross-fertilization reasonable integration context information natural human-machine interaction emotion class exciting landscape great importance late last decade realistic out-of-the-lab condition novel impulse spoken language next step approach towards speech emotion recognition robust recognition strong need classification method isolated individual group future direction research community emotional aspect feature selection certain extent research activity
