We present an analysis of prediction intervals for a non-intrusive method to estimate the clarity index (C50). The method employed to estimate C50 is a data driven approach that extracts multiple features from a reverberant speech signal which are then used to train a bidirectional long-short term memory model which maps the feature space into the target C50 value. The prediction intervals are derived from the standard deviation of the per-frame C50 estimates. This approach was shown to provide a coverage probability of 80%, i.e. 80% of times the ground truth lies between the estimated intervals, where the interval bounds are computed by using 5.6 times the standard deviation of the per-frame estimates. This accuracy is shown to be consistent with other noisy reverberant environments.

Authors: Peso Parada, Pablo; Sharma, Dushyant; Naylor, Patrick A.; van Waterschoot, Toon
Affiliations: Imperial College London, London, UK; KU Leuven, Leuven, Belgium; Nuance Communications, Inc., Marlow, UK(See document for exact affiliation information.)
AES Conference: 60th International Conference: DREAMS (Dereverberation and Reverberation of Audio, Music, and Speech) (January 2016)
Paper Number: 5-2
Publication Date: January 27, 2016 Import into BibTeX
Subject: Paper Session 5
Permalink: https://www.aes.org/e-lib/browse.cfm?elib=18077

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