Performance of Mask Based Statistical Beamforming in a Smart Home Scenario

Abstract

Mask based statistical beamforming, where signal statistics for the target and the interference gained from masking are used for beamforming, has shown great effectiveness in the two recent CHiME challenges. This idea has sparked interest in the research community and resulted in numerous proposed approaches based on the idea. At the same time, the advent of voice controlled smart home devices, such as Google Home and Amazon Alexa, has strengthened the need for robust far-field automatic speech recognition. In this paper, we evaluate if mask based beamforming can live up to the expectations created by the CHiME challenges and provide similar gains in a smart home scenario. To this extend, we pinpoint the main differences between the scenarios, review the recent developments and conduct extensive experiments on large scale data. These experiments show that, while a 10 % relative reduction of the word error rate can be achieved, the gains are not as high as those seen in the CHiME challenge. We also show that approaches where the frontend and back-end is trained jointly do not reach the performance level of their independently trained counterparts. On the plus side, we see a 20 % relative improvement for an evaluation set with crosstalk.