An Evaluation of Intrusive Instrumental Intelligibility Metrics

Steven Van Kuyk*, W. Bastiaan Kleijn, Richard Christian Hendriks

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

38 Citations (Scopus)
43 Downloads (Pure)


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.

Original languageEnglish
Article number8411476
Pages (from-to)2153-2166
Number of pages14
JournalIEEE/ACM Transactions on Audio Speech and Language Processing
Issue number11
Publication statusPublished - 2018

Bibliographical note

Accepted author manuscript


  • instrumental measures
  • Intelligibility prediction
  • speech enhancement


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