The Geneva Minimalistic Acoustic Parameter Set (GeMAPS) for Voice Research and Affective Computing

Florian Eyben, Klaus Scherer, Björn Schuller, Johan Sundberg, Elisabeth André, Carlos Busso, Laurence Devillers, Julien Epps, Petri Laukka, Shrikanth Narayanan, Khiet Phuong Truong

    Research output: Contribution to journalArticleAcademicpeer-review

    908 Citations (Scopus)
    380 Downloads (Pure)


    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.
    Original languageEnglish
    Pages (from-to)190-202
    Number of pages14
    JournalIEEE transactions on affective computing
    Issue number2
    Publication statusPublished - Apr 2016


    • Standard
    • Speech Analysis
    • EWI-26649
    • Acoustic Features
    • IR-98965
    • Geneva Minimalistic Parameter Set
    • Emotion Recognition
    • METIS-315136
    • Affective Computing
    • 2023 OA procedure

    Cite this