title: Text Injection for Capitalization and Turn-Taking Prediction in Speech
    Models
summary: 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.
paper_authors._id: 64dc797c0fd0857facb54ecd
paper_authors.name: Shaan Bijwadia
paper_authors._id: 64dc797c0fd0857facb54ece
paper_authors.name: Shuo-yiin Chang
paper_authors._id: 64dc797c0fd0857facb54ecf
paper_authors.name: Weiran Wang
paper_authors._id: 64dc797c0fd0857facb54ed0
paper_authors.name: Zhong Meng
paper_authors._id: 64dc797c0fd0857facb54ed1
paper_authors.name: Hao Zhang
paper_authors._id: 64dc797c0fd0857facb54ed2
paper_authors.name: Tara N. Sainath
paper_title: Text Injection for Capitalization and Turn-Taking Prediction in Speech
    Models
paper_summary: 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.
paper_ai_summary: Text injection using joint end-to-end and internal language model training enhances capitalization and turn-taking detection in automatic speech recognition systems.
paper_ai_keywords: joint end-to-end and internal language model training (JEIT) capitalization de-normalization turn-taking prediction automatic speech recognition (ASR)