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Heterogeneous Language Model Optimization in Automatic Speech Recognition

The rising data privacy risks make it difficult for automatic speech recognition (ASR) systems to acquire complete training data in practical application. Recently, the merge paradigm for acoustic model has been proposed to solve the issue. However, ASR still suffers from another salient issue on language model. Current efforts mainly focus on isomorphic neural network models, while language model optimization is characterized by merging and matching heterogeneous models including $n$-gram and neural network models. In this paper, we propose a novel Match-and-Merge paradigm to fill up the vacuum for the language model optimization. Based on different training datasets, we train multiple language model pairs. In order to merge them into a target pair with the best performance, we first propose a Genetic Match-and-Merge (GMM) method that can be specifically adopted to optimize heterogeneous models. To improve the algorithm efficiency, we further propose a Reinforced Match-and-Merge (RMM) method, which maintains superior recognition accuracy while reducing convergence time. Extensive experiments demonstrate the effectiveness and generalization of our proposed methods, which significantly establishes the new state-of-the-art.

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