Log-Energy Dynamic Range Normalizaton for Robust Speech Recognition
Weizhong Zhu, Douglas D. O’Shaughnessy · 2006
Cepstral mean normalization (CMN) has proved to be a simple noise robust feature processing technique. In its computation, the log-energy feature or C0 is treated in the same way as other cepstral coefficients. Mean normalization is not an effective way to remove effects of additive noise for the log-energy feature. We propose a log-energy dynamic range normalization (ERN) algorithm which normalizes log-energy sequences of an utterance to a target dynamic range. The AURORA 2.0 database together with HTK speech recognition toolkits are used to evaluate the proposed algorithm. The proposed algorithm improves the recognition result by 30.83% over the reference front-end algorithm in clean-condition training. It is superior to the result of CMN, which is 19.30%. It is also confirmed that this technique can be combined with CMN to achieve a 46.33% performance gain. The proposed algorithm is fairly simple and it only requires a very small extra computation load.