Print Email Facebook Twitter An Evaluation of Intrusive Instrumental Intelligibility Metrics Title An Evaluation of Intrusive Instrumental Intelligibility Metrics Author Van Kuyk, Steven (Victoria University of Wellington) Kleijn, W.B. (TU Delft Signal Processing Systems; Victoria University of Wellington) Hendriks, R.C. (TU Delft Signal Processing Systems) Date 2018 Abstract Instrumental intelligibility metrics are commonly used as an alternative to listening tests. This paper evaluates 12 monaural intrusive intelligibility metrics: SII, HEGP, CSII, HASPI, NCM, QSTI, STOI, ESTOI, MIKNN, SIMI, SIIB, and sEPSMcorr. In addition, this paper investigates the ability of intelligibility metrics to generalize to new types of distortions and analyzes why the top performing metrics have high performance. The intelligibility data were obtained from 11 listening tests described in the literature. The stimuli included Dutch, Danish, and English speech that was distorted by additive noise, reverberation, competing talkers, preprocessing enhancement, and postprocessing enhancement. SIIB and HASPI had the highest performance achieving a correlation with listening test scores on average of ρ =0.92 and ρ =0.89, respectively. The high performance of SIIB may, in part, be the result of SIIBs developers having access to all the intelligibility data considered in the evaluation. The results show that intelligibility metrics tend to perform poorly on datasets that were not used during their development. By modifying the original implementations of SIIB and STOI, the advantage of reducing statistical dependencies between input features is demonstrated. Additionally, this paper presents a new version of SIIB called SIIBGauss, which has similar performance to SIIB and HASPI, but takes less time to compute by two orders of magnitude. Subject instrumental measuresIntelligibility predictionspeech enhancement To reference this document use: http://resolver.tudelft.nl/uuid:2b452430-054c-43af-9544-bcf0b042996c DOI https://doi.org/10.1109/TASLP.2018.2856374 ISSN 2329-9290 Source IEEE - ACM Transactions on Audio, Speech, and Language Processing, 26 (11), 2153-2166 Bibliographical note Accepted author manuscript Part of collection Institutional Repository Document type journal article Rights © 2018 Steven Van Kuyk, W.B. Kleijn, R.C. Hendriks Files PDF An_Evaluation_of_Intrusiv ... etrics.pdf 2.35 MB Close viewer /islandora/object/uuid:2b452430-054c-43af-9544-bcf0b042996c/datastream/OBJ/view