Revisiting Some Model-Based and Data-Driven Denoising Algorithms in Aurora-2 Context

摘要

In this paper we evaluate some model-based and data-driven algorithms for robust speech recognition in noise, using the experimental framework provided by ETSI Aurora 2. Specifically, we focus on statistical linear approximation (SLA), sequential interacting multiple models (S-IMM), and histogram normalization (HN). As the baseline for the feature extraction scheme we use the ETSI front-end. Recognition tests on a subset of Aurora 2 show that SLA is approximately 4 % better than HN and that S-IMM is worse than HN by almost 3 % in terms of absolute word accuracy. A comparison with the ETSI advanced front-end (AFE) is also presented. While none of these algorithms outperforms AFE, we identify the reasons why this might have happened and point out potential directions for improvement.