Near end listening enhancement optimized with respect to Speech Intelligibility Index

Signal processing algorithms for near end listening enhancement allow to improve the intelligibility of clean (far end) speech for the near end listener who perceives not only the far end speech but also ambient background noise. A typical scenario is mobile communication conducted in the presence of acoustical background noise such as traffic or babble noise. In this contribution we analyze the calculation rules of the Speech Intelligibility Index (SII) and derive a simple condition for the speech spectrum level of every subband that maximizes the SII for a given noise spectrum level. This rule is used to derive a theoretical bound for a maximum achievable SII as well as a new SII optimized algorithm for near end listening enhancement. The impact of ignoring masking effects in the algorithm is also investigated and seconds our SNR recovery algorithm proposed earlier. Instrumental evaluation shows that the new algorithm performs close to the established theoretical bound.