Analysis and Extension of a Near-End Listening Enhancement Method Based on Long-Term Fractile Noise Statistics

This paper addresses the problem of near-end listening enhancement (NELE), where a clean speech signal is modified prior to playback and under an energy constraint to improve intelligibility in noise. We analyze a recently proposed NELE method, optimized using a Speech Intelligibility Index that has been modified to incorporate temporal aspects of the noise via long-term fractile noise statistics. Specifically, we explain the energy allocation strategy adopted by the algorithm, and show that, in contrast to many existing methods, the spectral energy distribution of the modified speech is a function of that of the background noise, but not that of the input speech. Our simulation experiments show that this simple method outperforms well-established spectral shaping NELE methods. In addition, we extend the algorithm by appending an off-the-shelf dynamic range compressor, and show that it performs generally better than state-of-the-art methods for NELE.