  Recent literature has shown that a learned front end with multi-channel audio
input can outperform traditional beam-forming algorithms for automatic speech
recognition (ASR). In this paper, we present our study on multi-channel
acoustic modeling using OPUS compression with different bitrates for the
different channels. We analyze the degradation in word error rate (WER) as a
function of the audio encoding bitrate and show that the WER degrades by 12.6%
relative with 16kpbs as compared to uncompressed audio. We show that its always
preferable to have a multi-channel audio input over a single channel audio
input given limited bandwidth. Our results show that for the best WER, when one
of the two channels can be encoded with a bitrate higher than 32kbps, its
optimal to encode the other channel with the highest bitrate possible. For
bitrates lower than that, its preferable to distribute the bitrate equally
between the two channels. We further show that by training the acoustic model
on mixed bitrate input, up to 50% of the degradation can be recovered using a
single model.
