Multiband with contaminated training data

In this paper, we present a new approach for improving the robustness of automatic speech recognition systems to additive noise. This approach lies on the use of a particular training procedure (based on data contamination) in a particular architecture (the multi-band paradigm). In this framework, we expect to remove the drawbacks of both the corpus contamination approach which is the dependency to noise spectral characteristics, and the multi-band architecture which is its inefficiency in case of wideband noise. This method has been tested on the AURORA 2 task and compared to other robust methods such as spectral subtraction, J-RASTA filtering and missing data compensation, leading to very good performance on different kinds of additive noise, without any a priori knowledge of the noise characteristics.