Evaluation of objective intelligibility prediction measures for noise-reduced signals in mandarin
Risheng Xia, Junfeng Li, Masato Akagi, Yonghong Yan · 2012
In this paper, the performance of eight state-of-the-art objective measures is evaluated in terms of predicting speech intelligibility in Mandarin of the processed signals by noise-reduction algorithms. The speech signals were first corrupted by three types of noises at two signal-to-noise ratios and subsequently processed by four classes of noise reduction algorithms, followed by objective intelligibility prediction. The subjective intelligibility ratings were obtained through a set of listening tests. Further investigation was conducted for objective measures in predicting speech intelligibility of noisy signals before and after noise-reduction processing in terms of correlation analysis and prediction errors. The analysis results reported here do provide valuable hints for analyzing and optimizing noise-reduction algorithms for Mandarin.