Distributed Training of Deep Neural Network Acoustic Models for Automatic Speech Recognition
This work addresses the need for efficient distributed training methods to handle large-scale data in ASR, but it is incremental as it reviews and compares existing strategies rather than introducing new ones.
The paper provides an overview of distributed training techniques for deep neural network acoustic models in automatic speech recognition, focusing on balancing communication and computation in high-performance computing environments, with experiments on a public benchmark to evaluate convergence, speedup, and recognition performance.
The past decade has witnessed great progress in Automatic Speech Recognition (ASR) due to advances in deep learning. The improvements in performance can be attributed to both improved models and large-scale training data. Key to training such models is the employment of efficient distributed learning techniques. In this article, we provide an overview of distributed training techniques for deep neural network acoustic models for ASR. Starting with the fundamentals of data parallel stochastic gradient descent (SGD) and ASR acoustic modeling, we will investigate various distributed training strategies and their realizations in high performance computing (HPC) environments with an emphasis on striking the balance between communication and computation. Experiments are carried out on a popular public benchmark to study the convergence, speedup and recognition performance of the investigated strategies.