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SpiN2017 - 9th Speech in Noise Workshop, 5-6 January 2017, Oldenburg, DE
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Modeling speech intelligibility based on envelopes derived from auditory spike trains Christoph Scheidiger(a), Johannes Zaar, Torsten DauDTU, Denmark(a) Presenting Speech intelligibility models aim to predict the human ability to understand speech in adverse listening conditions. The presented work combines the back-end processing of the multi-resolution speech-based envelope power spectrum model (mr-sEPSM; Jørgensen et al., 2013) with an auditory nerve (AN) model (Zilany and Bruce, 2014). The presented work calculated signal-noise-ratios in the envelope domain (SNRenv) for normal-hearing listeners based on different envelope representations derived from the AN model: (i) instantaneous firing rates (ii) peristimulus time histogram (PSTH) of auditory nerve spike trains, and (iii) SUMCOR neural metrics (Heinz and Swaminathan, 2009). The SNRenv patterns showed good agreements compared to the SNRenv patterns calculated from the acoustic (i.e. Hilbert) envelope (Heinz, 2...
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