StreamHear: Domain-Adapted Pseudo-Labeling for Semi-Supervised Streaming Speech Recognition
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Computer Science > Computation and Language
arXiv:2608.13717 (cs)
[Submitted on 13 Aug 2026]
Title:StreamHear: Domain-Adapted Pseudo-Labeling for Semi-Supervised Streaming Speech Recognition
View a PDF of the paper titled StreamHear: Domain-Adapted Pseudo-Labeling for Semi-Supervised Streaming Speech Recognition, by Zefang Liu and 5 other authors
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Abstract:Streaming automatic speech recognition (ASR) underperforms on domain-shifted target audio, where labeled in-domain data is costly to prepare while unlabeled audio is abundant. We present StreamHear, a semi-supervised pipeline that adapts a pretrained streaming student by fine-tuning an offline transducer teacher on the labeled training set, generating pseudo-labels on the unlabeled portion, and fine-tuning the student on the mixture. We further introduce a prior-regularized dynamic-programming realignment step that fixes chunk-level word placement using an ASR-hypothesis anchor. Across four datasets spanning financial calls, prepared read speech, and phone-quality dialogue, StreamHear consistently outperforms supervised student fine-tuning and narrows the gap to the offline teacher.
| Subjects: | Computation and Language (cs.CL); Audio and Speech Processing (eess.AS) |
| Cite as: | arXiv:2608.13717 [cs.CL] |
| (or arXiv:2608.13717v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2608.13717
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled StreamHear: Domain-Adapted Pseudo-Labeling for Semi-Supervised Streaming Speech Recognition, by Zefang Liu and 5 other authors
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