Accurate and compact large vocabulary speech recognition on mobile devices

In this paper we describe the development of an accurate, small-footprint, large vocabulary speech recognizer for mobile de-vices. To achieve the best recognition accuracy, state-of-the-art deep neural networks (DNNs) are adopted as acoustic models. A variety of speedup techniques for DNN score computation are used to enable real-time operation on mobile devices. To reduce the memory and disk usage, on-the-fly language model (LM) rescoring is performed with a compressed n-gram LM. We were able to build an accurate and compact system that runs well below real-time on a Nexus 4 Android phone. Index Terms: Deep neural networks, embedded speech recog-nition, SIMD, LM compression.