Speech intelligibility improvement in noisy environments based on energy correlation in frequency bands

Abstract

A new speech processing algorithm is proposed to improve speech intelligibility in noisy environments without increasing speech energy. The method improves the near-end speech intelligibility by optimizing the frame-based spectral energy correlation between clean speech and noisy modified speech with a power constraint. This algorithm is developed based on short-time objective intelligibility (STOI) measure, which predicts speech intelligibility in background noise according to the correlation of clean and noisy speeches. The proposed method is compared with unprocessed speech and two baseline methods using two objective intelligibility measures and an intelligibility listening test under various noisy conditions. Results show large intelligibility improvements with the proposed method over the unprocessed noisy speech. In addition, compared with the baseline methods, the proposed algorithm provides the best intelligibility scores for all noisy conditions in the STOI measure and for low signal-to-noise ratios in the speech intelligibility index. The word recognition results also show that the proposed algorithm performs better than the unprocessed speech and the reference methods. An objective quality measure is applied to investigate the speech quality of the introduced method, which is proven to insignificantly affect speech quality.