Submodular Rank Aggregation on Score-Based Permutations for Distributed Automatic Speech Recognition
Submodular Rank Aggregation on Score-Based Permutations for Distributed Automatic Speech Recognition
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 …