Streaming Punctuation: A Novel Punctuation Technique Leveraging Bidirectional Context for Continuous Speech Recognition

Understand

While speech recognition Word Error Rate (WER) has reached human parity for English, continuous speech recognition scenarios such as voice typing and meeting transcriptions still suffer from segmentation and punctuation problems, resulting from irregular pausing patterns or slow speakers.

• Transformer sequence tagging models are effective at capturing long bi-directional context, which is crucial for automatic punctuation.
• Automatic Speech Recognition (ASR) production systems, however, are constrained by real-time requirements, making it hard to incorporate the right context when making punctuation decisions.
• Context within the segments produced by ASR decoders can be helpful but limiting in overall punctuation performance for a continuous speech session.