On-Device End-to-end Speech Recognition with Multi-Step Parallel Rnns.

Most of the current automatic speech recognition is performed on a remote server. However, the demand for speech recognition on personal devices is increasing, owing to the requirement of shorter recognition latency and increased privacy. End-to-end speech recognition that employs recurrent neural networks (RNNs) shows good accuracy, but the execution of conventional RNNs, such as the long short-term memory (LSTM) or gated recurrent unit (GRU), demands many memory accesses, thus hindering its real-time execution on smart-phones or embedded systems. To solve this problem, we built an end-to-end acoustic model (AM) using linear recurrent units instead of LSTM or GRU and employed a multi-step parallel approach for reducing the number of DRAM accesses. The AM is trained with the connectionist temporal classification (CTC) loss, and the decoding is conducted using weighted finite-state transducers (WFSTs). The proposed system achieves x4.8 real-time speed when executed on a single core of an ARM CPU-based system.