Submodular Rank Aggregation on Score-based Permutations for Distributed Automatic Speech Recognition
Abstract
Distributed automatic speech recognition (ASR) requires to aggregate outputs of distributed deep neural network (DNN)-based models. This work studies the use of submodular functions to design a rank aggregation on score-based permutations, which can be used for distributed ASR systems in both supervised and unsupervised modes. Specifically, we compose an aggregation rank function based on the Lovasz Bregman divergence for setting up linear structured convex and nested structured concave functions. The algorithm is based on stochastic gradient descent (SGD) and can obtain well-trained aggregation models. Our experiments on the distributed ASR system show that the submodular rank aggregation can obtain higher speech recognition accuracy than traditional aggregation methods like Adaboost. Code is available online~\footnote{https://github.com/uwjunqi/Subrank}.
Cite
@article{arxiv.2001.10529,
title = {Submodular Rank Aggregation on Score-based Permutations for Distributed Automatic Speech Recognition},
author = {Jun Qi and Chao-Han Huck Yang and Javier Tejedor},
journal= {arXiv preprint arXiv:2001.10529},
year = {2021}
}
Comments
Accepted to ICASSP 2020. Please download the pdf to view Figure 1. arXiv admin note: substantial text overlap with arXiv:1707.01166