  Recognition of overlapped speech has been a highly challenging task to date.
State-of-the-art multi-channel speech separation system are becoming
increasingly complex and expensive for practical applications. To this end,
low-bit neural network quantization provides a powerful solution to
dramatically reduce their model size. However, current quantization methods are
based on uniform precision and fail to account for the varying performance
sensitivity at different model components to quantization errors. In this
paper, novel mixed precision DNN quantization methods are proposed by applying
locally variable bit-widths to individual TCN components of a TF masking based
multi-channel speech separation system. The optimal local precision settings
are automatically learned using three techniques. The first two approaches
utilize quantization sensitivity metrics based on either the mean square error
(MSE) loss function curvature, or the KL-divergence measured between full
precision and quantized separation models. The third approach is based on mixed
precision neural architecture search. Experiments conducted on the LRS3-TED
corpus simulated overlapped speech data suggest that the proposed mixed
precision quantization techniques consistently outperform the uniform precision
baseline speech separation systems of comparable bit-widths in terms of SI-SNR
and PESQ scores as well as word error rate (WER) reductions up to 2.88%
absolute (8% relative).
