7 research outputs found
Transfer learning of language-independent end-to-end ASR with language model fusion
This work explores better adaptation methods to low-resource languages using
an external language model (LM) under the framework of transfer learning. We
first build a language-independent ASR system in a unified sequence-to-sequence
(S2S) architecture with a shared vocabulary among all languages. During
adaptation, we perform LM fusion transfer, where an external LM is integrated
into the decoder network of the attention-based S2S model in the whole
adaptation stage, to effectively incorporate linguistic context of the target
language. We also investigate various seed models for transfer learning.
Experimental evaluations using the IARPA BABEL data set show that LM fusion
transfer improves performances on all target five languages compared with
simple transfer learning when the external text data is available. Our final
system drastically reduces the performance gap from the hybrid systems.Comment: Accepted at ICASSP201
O-1: Self-training with Oracle and 1-best Hypothesis
We introduce O-1, a new self-training objective to reduce training bias and
unify training and evaluation metrics for speech recognition. O-1 is a faster
variant of Expected Minimum Bayes Risk (EMBR), that boosts the oracle
hypothesis and can accommodate both supervised and unsupervised data. We
demonstrate the effectiveness of our approach in terms of recognition on
publicly available SpeechStew datasets and a large-scale, in-house data set. On
Speechstew, the O-1 objective closes the gap between the actual and oracle
performance by 80\% relative compared to EMBR which bridges the gap by 43\%
relative. O-1 achieves 13\% to 25\% relative improvement over EMBR on the
various datasets that SpeechStew comprises of, and a 12\% relative gap
reduction with respect to the oracle WER over EMBR training on the in-house
dataset. Overall, O-1 results in a 9\% relative improvement in WER over EMBR,
thereby speaking to the scalability of the proposed objective for large-scale
datasets