End-to-End Training of Acoustic Models for Large Vocabulary Continuous Speech Recognition with TensorFlow
Abstract
This article discusses strategies for end-to-end training of state-
of-the-art acoustic models for Large Vocabulary Continuous
Speech Recognition (LVCSR), with the goal of leveraging Ten-
sorFlow components so as to make efficient use of large-scale
training sets, large model sizes, and high-speed computation
units such as Graphical Processing Units (GPUs). Benchmarks
are presented that evaluate the efficiency of different approaches
to batching of training data, unrolling of recurrent acoustic
models, and device placement of TensorFlow variables and op-
erations. An overall training architecture developed in light of
those findings is then described. The approach makes it possi-
ble to take advantage of both data parallelism and high speed
computation on GPU for state-of-the-art sequence training of
acoustic models. The effectiveness of the design is evaluated
for different training schemes and model sizes, on a 20, 000
hour Voice Search task.
of-the-art acoustic models for Large Vocabulary Continuous
Speech Recognition (LVCSR), with the goal of leveraging Ten-
sorFlow components so as to make efficient use of large-scale
training sets, large model sizes, and high-speed computation
units such as Graphical Processing Units (GPUs). Benchmarks
are presented that evaluate the efficiency of different approaches
to batching of training data, unrolling of recurrent acoustic
models, and device placement of TensorFlow variables and op-
erations. An overall training architecture developed in light of
those findings is then described. The approach makes it possi-
ble to take advantage of both data parallelism and high speed
computation on GPU for state-of-the-art sequence training of
acoustic models. The effectiveness of the design is evaluated
for different training schemes and model sizes, on a 20, 000
hour Voice Search task.
Research Areas
