On The Compression Of Recurrent Neural Networks With An Application To LVCSR Acoustic Modeling For Embedded Speech Recognition
Abstract
We study the problem of compressing recurrent neural networks (RNNs). In particular, we focus on the compression
of RNN acoustic models, which are motivated by the goal
of building compact and accurate speech recognition systems
which can be run efficiently on mobile devices. In this work, we present a technique for general recurrent model compression that jointly compresses both recurrent and non-recurrent inter-layer weight matrices. We find that the proposed technique allows us to reduce the size of our Long Short-Term Memory (LSTM) acoustic model to a third of its original size with negligible loss in accuracy.
of RNN acoustic models, which are motivated by the goal
of building compact and accurate speech recognition systems
which can be run efficiently on mobile devices. In this work, we present a technique for general recurrent model compression that jointly compresses both recurrent and non-recurrent inter-layer weight matrices. We find that the proposed technique allows us to reduce the size of our Long Short-Term Memory (LSTM) acoustic model to a third of its original size with negligible loss in accuracy.
Research Areas
