Discriminative Methods for Noise Robust Speech Recognition: a Chime Challenge Benchmark

The recently introduced second CHiME challenge is a difficult two-microphone speech recognition task with non-stationary interference. Current approaches in the source-separation community have focused on the front-end problem of estimating the clean signal given the noisy signals. Here we pursue a different approach, focusing on state-of-the-art ASR techniques such as discriminative training and various feature transformations, in addition to simple noise suppression methods based on prior-based binary masking with estimated angle of arrival. In addition, we propose an augmented discriminative feature transformation that can introduce arbitrary features to a discriminative feature transform, an efficient combination method of Discriminative Language Modeling (DLM) and Minimum Bayes Risk (MBR) decoding in an ASR post-processing stage, and preliminarily investigate the effectiveness of deep neural networks for reverberated and noisy speech recognition. Using these techniques we present a benchmark on the middle-vocabulary subtask of CHiME challenge, showing their effectiveness for this task. Promising results were also obtained for the proposed augmented feature transformation and combination of DLM and MBR decoding. A part of the training code has been released as an advanced ASR baseline, using the Kaldi speech recognition toolkit.