Text Injection for Capitalization and Turn-Taking Prediction in Speech
  Models
Shaan Bijwadia Shuo-yiin Chang Weiran Wang Zhong Meng Hao Zhang Tara N. Sainath
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.