The number of end-to-end speech recognition models grows every year. These
models are often adapted to new domains or languages resulting in a
proliferation of expert systems that achieve great results on target data,
while generally showing inferior performance outside of their domain of
expertise. We explore combination of such experts via confidence-based
ensembles: ensembles of models where only the output of the most-confident
model is used. We assume that models' target data is not available except for a
small validation set. We demonstrate effectiveness of our approach with two
applications. First, we show that a confidence-based ensemble of 5 monolingual
models outperforms a system where model selection is performed via a dedicated
language identification block. Second, we demonstrate that it is possible to
combine base and adapted models to achieve strong results on both original and
target data. We validate all our results on multiple datasets and model
architectures.

本文探索基于置信度的集成模型方法，证明 5 种单语音模型组合胜于使用专用语言识别块的模型选择，同时结合基础模型和适应模型，可在数据原始状态和目标状态下获得强大结果。