Speeding up deep neural networks for speech recognition on ARM Cortex-A series processors

A new acoustic model based on deep neural network (DNN) has been introduced recently and outperforms the conventional Gaussian mixture model (GMM) in speech recognition on several tasks. However, the number of parameters required by a DNN model is much larger than that of its counterpart. The excessive cost of computation cumbers the implementation of DNN in many scenarios. In this paper, a DNN-based speech recognizer is implemented on an embedded platform. To reduce model size and computation cost, the DNN model is converted from float-point to fixed-point and NEON instructions are applied. The speed is further improved as a result of the downsizing of the DNN model via singular value decomposition (SVD) reconstruction. The work is evaluated on an ARM Cortex-A7 platform and 12x reduction of model size and 15x speedup of calculation are achieved without any noticeable accuracy loss.