An Evaluation of Emotion Units and Feature Types for Real-Time Speech Emotion Recognition

Autor:	Elisabeth André, Thurid Vogt
Rok vydání:	2011
Předmět:	Artificial Intelligence Computer science business.industry Speech recognition Recognition system Feature (machine learning) Artificial intelligence Emotion recognition ddc:004 business computer.software_genre computer Natural language processing
Zdroj:	KI - Künstliche Intelligenz. 25:213-223
ISSN:	1610-1987 0933-1875
DOI:	10.1007/s13218-011-0107-x
Popis:	Emotion recognition from speech in real-time is an upcoming research topic and the consideration of real-time constraints concerns all aspects of the recognition system. We present here a comparison of units and feature types for speech emotion recognition. To our knowledge, a comprehensive comparison of many different units on several databases is still missing in the literature and we also discuss units with special emphasis on real-time processing, that is, we do not only consider accuracy but also speed and ease of calculation. For the feature types, we also use only features that can be extracted fully automatically in real-time and look at which types best characterise which emotion classes. Gained insights are used as validation of methodology for our online speech emotion recognition system EmoVoice.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c67b3c877bb0727e0d29a6b804a20495 https://doi.org/10.1007/s13218-011-0107-x Zobrazit plný text záznamu Full text from SpringerLink