RNN-T Models Fail to Generalize to Out-of-Domain Audio: Causes and Solutions
Table 1: Non-streaming model WERs with regular inference (Reg.) and DOI on YT-short, YT-long and Call-center test sets. VN uses encoder variational noise (std=0.05) and SpecAugment uses 10 time masks up to 4% of audio length, 2 frequency masks up to 27 dims.
['Models', 'YT-short Reg.', 'YT-short DOI', 'YT-long Reg.', 'YT-long DOI', 'Call-center Reg.', 'Call-center DOI']
Non-Streaming Models: The results using the regularization cocktail and dynamic overlapping inference (DOI) are shown in Tab. All regularization techniques and their combinations help improve performance. In particular, SpecAugment + RSS + VN obtains a 14.2% improvement on YT-short and a 33.6% improvement on YT-long, and DOI obtains 8.5% and 23.8% improvement on YT-short and YT-long respectively. The proposed regularization cocktail improves WER by 30.1% and 18.8% when using regular inference and DOI, respectively. In general, DOI provides significant improvement when models have generalization issue on the target domain, and provide similar quality as regular inference for models that do no have this issue.