The Geneva Minimalistic Acoustic Parameter Set (GeMAPS) for Voice Research and Affective Computing
2016 (English)In: IEEE Transactions on Affective Computing, ISSN 1949-3045, E-ISSN 1949-3045, Vol. 7, no 2, 190-202 p.Article in journal (Refereed) Published
Work on voice sciences over recent decades has led to a proliferation of acoustic parameters that are used quite selectively and are not always extracted in a similar fashion. With many independent teams working in different research areas, shared standards become an essential safeguard to ensure compliance with state-of-the-art methods allowing appropriate comparison of results across studies and potential integration and combination of extraction and recognition systems. In this paper we propose a basic standard acoustic parameter set for various areas of automatic voice analysis, such as paralinguistic or clinical speech analysis. In contrast to a large brute-force parameter set, we present a minimalistic set of voice parameters here. These were selected based on a) their potential to index affective physiological changes in voice production, b) their proven value in former studies as well as their automatic extractability, and c) their theoretical significance. The set is intended to provide a common baseline for evaluation of future research and eliminate differences caused by varying parameter sets or even different implementations of the same parameters. Our implementation is publicly available with the openSMILE toolkit. Comparative evaluations of the proposed feature set and large baseline feature sets of INTERSPEECH challenges show a high performance of the proposed set in relation to its size.
Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2016. Vol. 7, no 2, 190-202 p.
Affective computing, acoustic features, standard, emotion recognition, speech analysis, geneva minimalistic parameter set
IdentifiersURN: urn:nbn:se:kth:diva-194029DOI: 10.1109/TAFFC.2015.2457417ISI: 000383995300007ScopusID: 2-s2.0-84973513831OAI: oai:DiVA.org:kth-194029DiVA: diva2:1037599
FunderEU, FP7, Seventh Framework Programme, 230331-PROPEREMO
QC 201610172016-10-172016-10-142016-10-17Bibliographically approved