Factorized Delayed Streams Modeling for LLM-based Streaming ASR

Abstract: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 on waiting steps. Experiments on the Corpus of Spontaneous Japanese and LibriSpeech show that F-DSM achieves better recognition performance than DSM. It also greatly reduces GPU memory use while maintaining similar training throughput, provides a small inference speed improvement through softmax skipping, and reduces the degradation in text-only perplexity observed with DSM.

Audio and Speech Processing (eess.AS); Computation and Language (cs.CL); Sound (cs.SD)

arXiv:2610.04333 [eess.AS]

(or arXiv:2610.04333v1 [eess.AS] for this version)

https://doi.org/10.48550/arXiv.2610.04333 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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