Predicting masking release of lateralized speech Alexandre Chabot-Leclerc, Ewen N. MacDonald, and Torsten Dau Hearing Systems group, Department of Electrical Engineering, Technical University of Denmark, DK-2800 Lyngby, Denmark alech@elektro.dtu.dk Background The binaural mr-sEPSM (Bin-sEPSM) is a binaural exten- sion of the multi-resolution speech-based envelop power spectrum model (mr-sEPM; Jørgensen et al., 2013). It uses a short-term equalization–cancellation process (EC) to model binaural unmasking. It can account for spatial masking release with different masker types (speech- shaped noise (SSN), speech-modulated SSN, multi-talker babble, and reversed-speech) and a number of maskers, in both anechoic and reverberant virtual environments (Hawley et al., 2004; Beutelmann et al., 2010). It can also account for masking release of lateralized speech with one SSN masker lateralized to the same, or oppo- site side of the target (L˝ocsei et al., 2015). The cur- rent study investigated the same lateralized condition, but with multiple reversed-speech maskers where none, some, or all maskers were presented on the same side as the target. The binaural mr-sEPSM Peripheral processing S+N Equalization Peripheral processing Cancellation Modulation ﬁltering SNRenv Binaural selector N (α0, τ0) S+N N … … … … Right ear Left ear Binaural path Intelligibility conversion Fig. 1: Structure of the binaural mr-sEPSM. … Two monaural mr-sEPSM (see Fig. 2 for overview). … An EC process with channel-independent pa- rameters, computed in 20 ms windows, for the binaural unmasking (BU) pathway (Wan et al., 2014). … A binaural selector which selects the best of the “better-ear” SNRenv (BE; between left and right) and of the BU SNRenv in the multi- resolution representation. … Conversion to intelligibility using a Gaussian psychometric function. Fig. 2: Structure of the monaural mr-sEPSM (repro- duced from Jørgensen et al., 2013). Results Fig. 3: Top panel: Measured SRTs (open squares) and predictions by the Bin-sEPSM (blue squares) and the ‘left-’ and ‘right-ear’ models (red triangles) for each con- dition. S conditions are with SSN maskers and C con- ditions are with reverse-babble maskers. Lower panel: Replot as masking release relative to the S11 condition. Table 1: Model performances. The bias is the mean difference between predictions and data SRTs, and r2 is the correlation coefﬁcient. Model Bias r2 Bin-sEPSM -2.17 0.78 Left and Right 1.22 0.53 Stimuli and conditions Notation: # of co-located maskers # all maskers - example A T M1 M2 T M1 M2 - example B T M1 M2 T M1 M2 Fig. 4: Each stimuli is steered to the left or right, using a 680 μs lead at one ear (marked by rectangles). The monaural SNR is the same in both examples. Figure from L˝ocsei et al. (2015). … SRTs measured by L˝ocsei et al. (2015) using the open-set DAT corpus (Nielsen et al., 2014). … Maskers: … SSN matched to target (S1); … 2/4/8 reversed male talkers from GRID sen- tences (Cooke et al., 2006) (C2/C4/C8). Acknowledgements This research was supported in part by the National Science and Engineering Research Council of Canada (NSERC), Phonak, and the Technical University of Denmark. Discussion … The left- and right-ear predictions depend only on the total number of maskers, conﬁrming the constant SNR and energetic masking (EM) on a given side. … BU processing is required for predicting a re- lease from masking between all-collocated and all-separated conditions, e.g., between C44 and C04. … The Bin-sEPSM does not capture the increased SRT in the C22 condition due to increased confu- sions between target and maskers. … It is assumed that the Bin-sEPSM has perfect segregation between the target and maskers because of its inputs (speech and noise mixture, and noise alone). Therefore, it might require an estimate of the similarity between target and maskers to account for confusions, which is con- sidered a source of informational masking (IM; Watson, 2005). Fig. 5: Euclidean distan