  Recently, speech recognition with ad-hoc microphone arrays has received much
attention. It is known that channel selection is an important problem of ad-hoc
microphone arrays, however, this topic seems far from explored in speech
recognition yet, particularly with a large-scale ad-hoc microphone array. To
address this problem, we propose a Scaling Sparsemax algorithm for the channel
selection problem of the speech recognition with large-scale ad-hoc microphone
arrays. Specifically, we first replace the conventional Softmax operator in the
stream attention mechanism of a multichannel end-to-end speech recognition
system with Sparsemax, which conducts channel selection by forcing the channel
weights of noisy channels to zero. Because Sparsemax punishes the weights of
many channels to zero harshly, we propose Scaling Sparsemax which punishes the
channels mildly by setting the weights of very noisy channels to zero only.
Experimental results with ad-hoc microphone arrays of over 30 channels under
the conformer speech recognition architecture show that the proposed Scaling
Sparsemax yields a word error rate of over 30% lower than Softmax on simulation
data sets, and over 20% lower on semi-real data sets, in test scenarios with
both matched and mismatched channel numbers.
