| 1. |
- Eyben, Florian, et al.
(författare)
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The Geneva Minimalistic Acoustic Parameter Set (GeMAPS) for Voice Research and Affective Computing
- 2016
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Ingår i: IEEE Transactions on Affective Computing. - : Institute of Electrical and Electronics Engineers (IEEE). - 1949-3045. ; 7:2, s. 190-202
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 2. |
- Pokorny, Florian B., et al.
(författare)
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Earlier Identification of Children with Autism Spectrum Disorder : An Automatic Vocalisation-based Approach
- 2017
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Ingår i: 18Th Annual Conference Of The International Speech Communication Association (INTERSPEECH 2017), Vols 1-6. - 9781510848764 ; , s. 309-313
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Konferensbidrag (refereegranskat)abstract
- Autism spectrum disorder (ASD) is a neurodevelopmental disorder usually diagnosed in or beyond toddlerhood. ASD is defined by repetitive and restricted behaviours, and deficits in social communication. The early speech-language development of individuals with ASD has been characterised as delayed. However, little is known about ASD-related characteristics of pre-linguistic vocalisations at the feature level. In this study. we examined pre-linguistic vocalisations of 10-month-old individuals later diagnosed with ASD and a matched control group of typically developing individuals (N = 20). We segmented 684 vocalisations from parent-child interaction recordings. All vocalisations were annotated and signal-analytically decomposed. We analysed ASD-related vocalisation specificities on the basis of a standardised set (eGeMAPS) of 88 acoustic features selected for clinical speech analysis applications. 54 features showed evidence for a differentiation between vocalisations of individuals later diagnosed with ASD and controls. In addition, we evaluated the feasibility of automated, vocalisation-based identification of individuals later diagnosed with ASD. We compared linear kernel support vector machines and a 1-layer bidirectional long short-term memory neural network. Both classification approaches achieved an accuracy of 75% for subject-wise identification in a subject-independent 3-fold cross-validation scheme. Our promising results may be an important contribution en-route to facilitate earlier identification of ASD.
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