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Distributed Submodular Maximization for Large Vocabulary Continuous Speech Recognition

Huge training datasets for automatic speech recognition (ASR) typically contain redundant information so that a subset of data is generally enough to obtain similar ASR performance to that obtained when the entire dataset is employed for training. Although the centralized submodular-based data selection methods have been successfully applied to obtain a representable subset involving the most significant information of the whole dataset, the submodular data selection conveys problems in adapting to an extremely massive dataset. This paper proposes to use distributed submodular maximization (DSM) for efficiently selecting a data subset that maintains the ASR performance, while reducing tremendously the computational overhead. There are two approaches for the distributed submodular maximization problem: one is based on an homogeneous submodular function, and the other relies on decomposable submodular functions in which heterogeneous submodular functions are applied. Our experiments show that the data subset output by the DSM algorithms can maintain the ASR performance, while significantly reducing the computational overhead. 1