A Speech Intelligibility Index-based approach to predict the speech reception threshold for sentences in fluctuating noise for normal-hearing listeners.

By K. S. Rhebergen, N. Versfeld. Journal of the Acoustical Society of America, 2005

316 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Speech and Hearing, Signal Processing

View Paper: https://www.semanticscholar.org/paper/a78f147e51e40cd99d4a90dcd223d0aa56fc34ea

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Citations in the corpus

Factors Affecting Speech Understanding in Older Adults (2010, background): “It has not been developed for application to many other forms of temporally distorted speech, such as interrupted or time-compressed speech, or for temporally fluctuating background noise, although there have been attempts to extend the framework to at least some of these cases (e.g., Dubno et al. 2”
Objective measures for predicting speech intelligibility in noisy conditions based on new band-importance functions. (2009, background): “…speech intelligibility better than the traditional AI measure when evaluated with sentences embedded in artificial masking signals e.g., periodically interrupted noise and speech-like maskers, but the predictions with the latter maskers were found to be less accurate Rhebergen and Versfeld, 2005 .”
An Algorithm for Predicting the Intelligibility of Speech Masked by Modulated Noise Maskers (2016, methodology): “The implementation uses stationary speech-shaped Gaussian noise instead of undistorted real speech signals as input (as speciﬁed in [3], [6]), but excludes the upward-spread-of-masking functionality as de-ﬁned in [3], because this appears to degrade performance.”
A short-time objective intelligibility measure for time-frequency weighted noisy speech (2010, background): “[5, 6] and STI (see [7] for an overview of STI-based measures).”
On the information rate of speech communication (2017, background): “The resulting relation in [7] between intelligibility and environmental noise spectra closely resembles that of the heuristically derived measures such as the articulation index (AI), e.g., [8, 9], and the speech intelligibility index (SII) [10, 11].”