## 4 Discussion  
In this study a new reverb-based NELE approach based on the optimization of a cost function was proposed that reduces the time-smearing effect of reverberation on speech and similar to the OE amplifies the onsets and has high-pass filter characteristics. Its main advantage to the OE is considering future frames in finding filtering weights and for this reason, more reduction of the overlap masking of reverberation tail is seen. The amount of overlap masking is considered in the defined cost function and is used to control the weights applied on the original speech signal speech segments that would make the upcoming frames inaudible. Higher importance for the onset segments in the cost function is assigned to avoid attenuation of onsets which would decrease intelligibility. Both the model predictions and listening-test results showed improvement in SRTs. It has been demonstrated by the model prediction that the proposed algorithm is better able to compensate for the detrimental effects of reverberation than the OE method. The subjective evaluation showed that depending on the scenario there is an improvement  
Figure 11: (**a**) Boxplots of speech reception thresholds at SRT\({}_{50}\) for 8 subjects to evaluate the robustness of preprocessing methods in the presence of pink noise (PN) are illustrated. The unprocessed and the preprocessed signals for the main scenarios using the OE and proposed method are compared; (**b**) The mean-SRT\({}_{50}\) and the standard errors for the same data in (**a**) are plotted. Figure 10: (**a**) Boxplots of speech reception thresholds at SRT\({}_{50}\) for 8 subjects to evaluate the robustness of preprocessing methods, measured in the presence of SSN are illustrated. The unprocessed and the preprocessed signals for the robustness evaluation scenarios using the OE and proposed method are compared; (**b**) The mean-SRT\({}_{50}\) and the standard errors for the same data are plotted. of 0.5 dB up to 2 dB in comparison to the OE. The mr-GPSM model predicts a larger improvement for the proposed method over the OE method than what is actually observed in the listening tests. One reason could be the stronger artifacts created by the proposed algorithm compared to the OE method. A possible modification of the method could be using a quality-assessment criteria in the optimization procedure. The algorithm evaluation using both the intelligibility models and the listening test showed that improvements in speech intelligibility did not depend on having an exact match between the positions of the source and the listener used for obtaining the optimal weights. Similar to the OE approach, this underlines the robustness of this algorithm for errors in the estimation of room impulse responses. For the proposed algorithm, only a course spectro-temporal representation of the room impulse response is used and the exact magnitudes and phases of the transfer function are not needed. Therefore, the robustness problem that exists in the inverse-filtering approaches is avoided in the proposed method. Another important point about the proposed approach is the fixed parameters being used in construction of the cost function and optimization. The fixed set of parameters is used for all of the scenarios. The performance of the algorithm is dependent on the parameters that are set. The first parameter is the number of the future frames (\(P\)) that are considered in the summed-cost-function (SCF) of Equation (12) which is based on the reverberation time. To reduce the computational load, it could set much less than the number of frames covered by the \(T_{60}\). Informal listening tests showed that beyond a specific number of future frames, the signal is not much more improved. Imperially, \(P\) was determined according to the EDC of the RIR, until the point it drops 25 dB. Other parameters that are fixed empirically are the weights (\(\beta_{i}\)) assigned to onsets in the defined SCF again in Equation (12) and the lower and upper bound for the gains in Equation (13).