aiqbench analysis
FormalASR introduces compact end-to-end models that directly transcribe spoken Chinese into formal written text, eliminating the latency and memory overhead of traditional two-stage ASR+LLM pipelines. By leveraging large-scale datasets constructed via LLM-based rewriting, the approach achieves significant reductions in character error rates while enabling lightweight on-device deployment without post-processing. This advancement addresses a key bottleneck for writing-oriented applications by removing disfluencies at the inference stage rather than through costly downstream editing.
FormalASR: End-to-End Spoken Chinese to Formal Text
arXiv:2605.19266v2 Announce Type: replace Abstract: Automatic speech recognition (ASR) systems are typically optimized for verbatim transcription, which preserves disfluencies, filler words, and informal spoken structures that are often unsuitable for downstream writing-oriented applications. A common workaround is a two-stage ASR+LLM pipeline for post-editing, but this design increases latency and memory cost and is difficult to deploy on-device. We present FormalASR, two compact end-to-end models (0.6B and 1.7B) that directly transcribe spoken Chinese into formal written text. To enable this setting, we build WenetSpeech-Formal and Speechio-Formal, two large-scale spoken-to-formal datasets constructed by LLM-based rewriting and quality filtering. We then fine-tune Qwen3-ASR at two scales (0.6B and 1.7B) with supervised fine-tuning. Experiments on WenetSpeech-Formal and Speechio-Formal show that FormalASR achieves up to 37.4% relative CER reduction over verbatim baselines, while also improving ROUGE-L and BERTScore. FormalASR requires no post-processing LLM at deployment time, providing a lightweight, on-device solution for spoken-to-formal transcription.
Abstract:Automatic speech recognition (ASR) systems are typically optimized for verbatim transcription, which preserves disfluencies, filler words, and informal spoken structures that are often unsuitable for downstream writing-oriented applications. A common workaround is a two-stage ASR+LLM pipeline for post-editing, but this design increases latency and memory cost and is difficult to deploy on-device. We present FormalASR, two compact end-to-end models (0.6B and 1.7B) that directly transcribe spoken Chinese into formal written text. To enable this setting, we build WenetSpeech-Formal and Speechio-Formal, two large-scale spoken-to-formal datasets constructed by LLM-based rewriting and quality filtering. We then fine-tune Qwen3-ASR at two scales (0.6B and 1.7B) with supervised fine-tuning. Experiments on WenetSpeech-Formal and Speechio-Formal show that FormalASR achieves up to 37.4% relative CER reduction over verbatim baselines, while also improving ROUGE-L and BERTScore. FormalASR requires no post-processing LLM at deployment time, providing a lightweight, on-device solution for spoken-to-formal transcription.
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
arXiv:2605.19266 [cs.CL]
(or arXiv:2605.19266v2 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2605.19266 Focus to learn more arXiv-issued DOI via DataCite
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