  The intensive computation of Automatic Speech Recognition (ASR) models
obstructs them from being deployed on mobile devices. In this paper, we present
a novel quantized Winograd optimization pipeline, which combines the
quantization and fast convolution to achieve efficient inference acceleration
on mobile devices for ASR models. To avoid the information loss due to the
combination of quantization and Winograd convolution, a Range-Scaled
Quantization (RSQ) training method is proposed to expand the quantized
numerical range and to distill knowledge from high-precision values. Moreover,
an improved Conv1D equipped DFSMN (ConvDFSMN) model is designed for mobile
deployment. We conduct extensive experiments on both ConvDFSMN and Wav2letter
models. Results demonstrate the models can be effectively optimized with the
proposed pipeline. Especially, Wav2letter achieves 1.48* speedup with an
approximate 0.07% WER decrease on ARMv7-based mobile devices.
