Improved noise robustness by corrective and rival training

We show that discriminative training methods have the potential to improve noise robustness even for high resolution acoustic models trained on noisy data. To this end, we compare the performance of acoustic models trained on noisy data using maximum likelihood (ML), corrective (CT) and rival training (RT). Experiments are performed on a German and a Dutch continuous digit string recognition task, yielding improvements in the range of 12% to 35% relative.