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Incremental learning is one paradigm to enable model building and updating atscale with streaming data. For end-to-end automatic speech recognition (ASR)tasks, the absence of human annotated labels along with the need for privacypreserving policies for model building makes it a daunting challenge. Motivatedby these challenges, in this paper we use a cloud based framework forproduction systems to demonstrate insights from privacy preserving incrementallearning for automatic speech recognition (ILASR). By privacy preserving, wemean, usage of ephemeral data which are not human annotated. This system is astep forward for production levelASR models for incremental/continual learningthat offers near real-time test-bed for experimentation in the cloud forend-to-end ASR, while adhering to privacy-preserving policies. We show that theproposed system can improve the production models significantly(3%) over a newtime period of six months even in the absence of human annotated labels withvarying levels of weak supervision and large batch sizes in incrementallearning. This improvement is 20% over test sets with new words and phrases inthe new time period. We demonstrate the effectiveness of model building in aprivacy-preserving incremental fashion for ASR while further exploring theutility of having an effective teacher model and use of large batch sizes.

ILASR: Privacy-Preserving Incremental Learning for Automatic Speech Recognition at Production Scale