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 (Grosse, van de Par, 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.
Highlights
In conventional speech enhancement methods, the speech signal is recovered from a mixture of reverberation and noise
Speech shaped noise (SSN) and pink noise (PN) are used as the interferers which are convolved with binaural room impulse response (BRIR) and presented at an average level of 65 dB-SPL for left and right ears
The cochleagram of a clean speech from the OLSA corpus and two preprocessed speech signals, one of them preprocessed with the Onset Enhancement (OE) algorithm [16] and another one preprocessed by the proposed algorithm, are depicted in Figure 3a.The weights are calculated for room R4
Summary
In conventional speech enhancement methods, the speech signal is recovered from a mixture of reverberation and noise. The intelligibility-improving signal processing approach (IISPA) [11] is another DNN-based method that uses an automatic-speech recognitionbased 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, in the presence of non-stationary noise.
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