  In this paper, we propose and investigate a variety of distributed deep
learning strategies for automatic speech recognition (ASR) and evaluate them
with a state-of-the-art Long short-term memory (LSTM) acoustic model on the
2000-hour Switchboard (SWB2000), which is one of the most widely used datasets
for ASR performance benchmark. We first investigate what are the proper
hyper-parameters (e.g., learning rate) to enable the training with sufficiently
large batch size without impairing the model accuracy. We then implement
various distributed strategies, including Synchronous (SYNC), Asynchronous
Decentralized Parallel SGD (ADPSGD) and the hybrid of the two HYBRID, to study
their runtime/accuracy trade-off. We show that we can train the LSTM model
using ADPSGD in 14 hours with 16 NVIDIA P100 GPUs to reach a 7.6% WER on the
Hub5- 2000 Switchboard (SWB) test set and a 13.1% WER on the CallHome (CH) test
set. Furthermore, we can train the model using HYBRID in 11.5 hours with 32
NVIDIA V100 GPUs without loss in accuracy.

Sound;Computation and Language;Machine Learning;Audio and Speech Processing;Machine Learning