Personalized speech recognition on mobile devices
We describe a large vocabulary speech recognition system that is accurate, has low latency, and yet has a small enough memory and computational footprint to run faster than real time on a Nexus 5 Android smartphone. We employ a quantized Long Short Term Memory (LSTM) acoustic model trained with connectionist temporal classification (CTC) to directly predict phoneme targets, and further reduce its memory footprint using an SVD based compression scheme. Additionally, we minimize our memory footprint by using a single language model for both dictation and voice command domains, constructed using Bayesian interpolation. Finally, in order to properly handle device specific information, such as proper names and other context dependent information, we inject vocabulary items into the decoder graph and bias the language model on the fly. Our system achieves 13.5% word error rate on an open ended dictation task, running with a median speed that is seven times faster than real time.
2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)