16 research outputs found
The Gift of Feedback: Improving ASR Model Quality by Learning from User Corrections through Federated Learning
Automatic speech recognition (ASR) models are typically trained on large
datasets of transcribed speech. As language evolves and new terms come into
use, these models can become outdated and stale. In the context of models
trained on the server but deployed on edge devices, errors may result from the
mismatch between server training data and actual on-device usage. In this work,
we seek to continually learn from on-device user corrections through Federated
Learning (FL) to address this issue. We explore techniques to target fresh
terms that the model has not previously encountered, learn long-tail words, and
mitigate catastrophic forgetting. In experimental evaluations, we find that the
proposed techniques improve model recognition of fresh terms, while preserving
quality on the overall language distribution.Comment: Accepted to IEEE ASRU 202
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