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 conn...

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Bibliographic Details
Main Authors: McGraw, Ian, Prabhavalkar, Rohit, Alvarez, Raziel, Gonzalez Arenas, Montse, Rao, Kanishka, Rybach, David, Alsharif, Ouais, Sak, Hasim, Gruenstein, Alexander, Beaufays, Francoise, Parada, Carolina
Format: Conference Proceeding
Language:English
Subjects:
Acoustics
Adaptation models
Computational modeling
Context
CTC
Electronics
embedded speech recognition
Footprints
LSTM
Mathematical models
model compression
quantization
Real time
Smartphones
Speech
Speech recognition
Training
Voice
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Summary: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.
ISSN:2379-190X
DOI:10.1109/ICASSP.2016.7472820