Rich sources of variability in natural speech present significant challenges
to current data intensive speech recognition technologies. To model both
speaker and environment level diversity, this paper proposes a novel Bayesian
factorised speaker-environment adaptive training and test time adaptation
approach for Conformer ASR models. Speaker and environment level
characteristics are separately modeled using compact hidden output transforms,
which are then linearly or hierarchically combined to represent any
speaker-environment combination. Bayesian learning is further utilized to model
the adaptation parameter uncertainty. Experiments on the 300-hr WHAM noise
corrupted Switchboard data suggest that factorised adaptation consistently
outperforms the baseline and speaker label only adapted Conformers by up to
3.1% absolute (10.4% relative) word error rate reductions. Further analysis
shows the proposed method offers potential for rapid adaption to unseen
speaker-environment conditions.

本文提出了一种新的基于贝叶斯因式分解的说话人和环境自适应训练和测试方法，用于 Conformer ASR 模型，实验结果表明，该方法在 WHAM 噪声下的 Switchboard 数据中优于基线模型和仅适配说话人标签的 Conformer 模型，可以实现快速适应无见说话人和环境条件。