###### Abstract  
Speech intelligibility in public places can be degraded by the environmental noise and reverberation. In this study, a new near-end listening enhancement (NELE) approach is proposed in which using a time varying filter jointly enhances the onsets and reduces the overlap masking. For optimization, some look-ahead in clean speech and prior knowledge of room impulse response (RIR) are required. In this method, by optimizing a defined cost function, the Spectro-Temporal Envelope of reverb speech is optimized to be as close as possible to that of clean speech. In this cost function, onsets of speech are optimized with increased weight. This approach is different from overlap-masking ratio (OMR) and speech enhancement (OE) approaches ([PERSON], [PERSON], 2017, J. Audio Eng. Soc., Vol. 65 (1/2), pp. 31-41) that only consider previous frames in each time slot for determining the time variant filtering. The SRT measurements show that the new optimization framework enhances the speech intelligibility up to 2 dB more that OE. speech enhancement; NELE; reverberation; speech intelligibility; optimization +
Footnote †: 2021: 11, 10788. [[https://doi.org/10.3390/app112210788](https://doi.org/10.3390/app112210788)]([https://doi.org/10.3390/app112210788](https://doi.org/10.3390/app112210788))  
Automatic Sound Engineer (ASE) is proposed in which using equalization and broadband compression maximizes speech intelligibility while keeping a good sound quality [5]. In the second type of NELE methods, only the presence of noise is taken into account in the development of an enhancement algorithm. Speech modifications in these methods are usually based on an objective speech intelligibility measure, e.g., the speech intelligibility index (SII) [5] or the glimpse proportion (GP) [6] which is used as an optimization target. Similar to the SSDRC, most of the successful noise-dependent algorithms use DRC in the time domain to enhance the consonants intelligibility. In adaptDRC [6; 7], spectral modification and dynamic time compressing are performed to improve the SII in the presence of additive noise. In this method, although the impact of reverberation is not explicitly considered in the enhancement procedure, nevertheless considerable enhancement is reported in the intelligibility in the presence of reverberation [8]. Another noise-dependent NELE approach is based on improving the STOI score [9]. In some of the noise-dependent methods, deep neural networks (DNNs) are used to modify speech energy. In a method called iMetricGAN [10], the enhancement is performed by repeated predictions of the intelligibility score of modified speech and producing scale factors that multiplied on the unmodified spectrogram. The intelligibility-improving signal processing approach (IISPA) [11] is another DNN-based method that uses an automatic-speech recognition-based model of speech perception to optimize different parameters such as band-pass edge frequencies, spectral slope and curvature, and spectral modulation compression or expansion. Note that in these noise-dependent methods, the quality of speech can degrade strongly, specifically in the presence of non-stationary noise. In the third category of NELE methods named reverberation-dependent, room impulse response (RIR) data are explicitly considered in the modification procedure ([12; 13; 14; 15]). [PERSON] and [PERSON] (2017) [16] proposed two methods namely, OE (Onset Enhancement) and Overlap Masking Ratio (OMR)--which were inspired by previous studies in [17; 18]. In these two approaches, having access to a RIR, time varying gains are calculated for each frame, based on the energy of current frame and that of the previous frames of speech. In the current study, a reverberation-dependent approach is developed to optimize the Spectro-Temporal Envelope of a reverberated speech by onset enhancement and also by reducing the amount of overlap-masking.