Word Timestamps and Speaker Attribution with a Non-Autoregressive LLM

Timestamps and speaker attribution are useful additions to speech recognition, creating a rich text transcript. This information can either be extracted during transcription or aligned to a given transcript. In this paper we present models that add timestamps and speaker information to a given transcript using a non-autoregressive LLM-based architecture. Compared to an autoregressive model built from similar components, the models are more accurate and annotate a given transcript one to two orders of magnitude faster. Compared to other models, our models achieve state-of-the-art timestamp accuracy and the best cpWER for speaker attribution.