Personalized Speech recognition on mobile devices
This work addresses the challenge of deploying personalized speech recognition on resource-constrained smartphones, offering incremental improvements in efficiency and adaptability.
The paper tackles the problem of enabling accurate, low-latency, and memory-efficient large vocabulary speech recognition on mobile devices, achieving a 13.5% word error rate on dictation tasks with a median speed seven times faster than real-time.
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.