Mask The Bias: Improving Domain-Adaptive Generalization of CTC-based ASR
  with Internal Language Model Estimation
Nilaksh Das Monica Sunkara Sravan Bodapati Jinglun Cai Devang Kulshreshtha Jeff Farris Katrin Kirchhoff
End-to-end ASR models trained on large amount of data tend to be implicitly
biased towards language semantics of the training data. Internal language model
estimation (ILME) has been proposed to mitigate this bias for autoregressive
models such as attention-based encoder-decoder and RNN-T. Typically, ILME is
performed by modularizing the acoustic and language components of the model
architecture, and eliminating the acoustic input to perform log-linear
interpolation with the text-only posterior. However, for CTC-based ASR, it is
not as straightforward to decouple the model into such acoustic and language
components, as CTC log-posteriors are computed in a non-autoregressive manner.
In this work, we propose a novel ILME technique for CTC-based ASR models. Our
method iteratively masks the audio timesteps to estimate a pseudo
log-likelihood of the internal LM by accumulating log-posteriors for only the
masked timesteps. Extensive evaluation across multiple out-of-domain datasets
reveals that the proposed approach improves WER by up to 9.8% and OOV F1-score
by up to 24.6% relative to Shallow Fusion, when only text data from target
domain is available. In the case of zero-shot domain adaptation, with no access
to any target domain data, we demonstrate that removing the source domain bias
with ILME can still outperform Shallow Fusion to improve WER by up to 9.3%
relative.