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abstract/2308.07395.txt

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+Text injection for automatic speech recognition (ASR), wherein unpaired
+text-only data is used to supplement paired audio-text data, has shown
+promising improvements for word error rate. This study examines the use of text
+injection for auxiliary tasks, which are the non-ASR tasks often performed by
+an E2E model. In this work, we use joint end-to-end and internal language model
+training (JEIT) as our text injection algorithm to train an ASR model which
+performs two auxiliary tasks. The first is capitalization, which is a
+de-normalization task. The second is turn-taking prediction, which attempts to
+identify whether a user has completed their conversation turn in a digital
+assistant interaction. We show results demonstrating that our text injection
+method boosts capitalization performance for long-tail data, and improves
+turn-taking detection recall.