Modern Automatic Speech Recognition (ASR) systems often use a portfolio of
domain-specific models in order to get high accuracy for distinct user
utterance types across different devices. In this paper, we propose an
innovative approach that integrates the different per-domain per-device models
into a unified model, using a combination of domain embedding, domain experts,
mixture of experts and adversarial training. We run careful ablation studies to
show the benefit of each of these innovations in contributing to the accuracy
of the overall unified model. Experiments show that our proposed unified
modeling approach actually outperforms the carefully tuned per-domain models,
giving relative gains of up to 10% over a baseline model with negligible
increase in the number of parameters.

本文提出一种创新的方法，将不同领域和不同设备的模型整合成一个统一的模型，使用领域嵌入、领域专家、专家混合和对抗性训练来提高整体统一模型的准确性，并通过仔细的消融研究表明每个创新的好处，实验证明我们提出的统一建模方法实际上优于精心调整的专业领域模型。