Factorized Delayed Streams Modeling for LLM-based Streaming ASR
Delayed Streams Modeling (DSM) enables LLM-based streaming automatic speech recognition (ASR) by aligning acoustic and text streams on a common timeline. DSM adds the padding token <p> and the word-start token <w> to the LLM vocabulary and predicts them together with normal text tokens using the same softmax. We first show that <w> can be removed while maintaining competitive recognition performance. Based on this result, we propose Factorized DSM (F-DSM), which separates the waiting probability for <p> from the distribution over the original LLM vocabulary. This factorization removes ASR-specific tokens from the text prediction space and allows the large-vocabulary softmax to be skipped ...
| Authors | Tatsunari Takagi, Kai Washizaki, Atsushi Kojima et al. |
|---|---|
| Primary category | eess.AS |
| Published | 3 October 2026 |
| Last updated at source | 3 October 2026 |
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