The deep neural network (DNN) based speech enhancement approaches have
achieved promising performance. However, the number of parameters involved in
these methods is usually enormous for the real applications of speech
enhancement on the device with the limited resources. This seriously restricts
the applications. To deal with this issue, model compression techniques are
being widely studied. In this paper, we propose a model compression method
based on matrix product operators (MPO) to substantially reduce the number of
parameters in DNN models for speech enhancement. In this method, the weight
matrices in the linear transformations of neural network model are replaced by
the MPO decomposition format before training. In experiment, this process is
applied to the causal neural network models, such as the feedforward multilayer
perceptron (MLP) and long short-term memory (LSTM) models. Both MLP and LSTM
models with/without compression are then utilized to estimate the ideal ratio
mask for monaural speech enhancement. The experimental results show that our
proposed MPO-based method outperforms the widely-used pruning method for speech
enhancement under various compression rates, and further improvement can be
achieved with respect to low compression rates. Our proposal provides an
effective model compression method for speech enhancement, especially in
cloud-free application.

该论文提出了一种基于 MPO 的模型压缩方法，在神经网络模型的线性变换中将权重矩阵替换为 MPO 分解格式。实验结果表明，该方法在各种压缩率下均优于常用的修剪方法，特别适用于云端应用中的语音增强。