Ruby-ASR: Evidence-Preserving Supervision for Joint Orthographic and Lexical-Reading Recognition

2026-09-22

AI Summary

为解决日语语音识别中词汇读音区分问题，提出Ruby-ASR方法，通过结合正字法和词汇读音序列提高识别准确性。

Abstract

Conventional Japanese automatic speech recognition (ASR) is supervised by an orthographic transcript, although the same written form can correspond to different lexical readings realized in speech. Such utterances receive an identical target, so their reading distinction is absent from the supervision interface and cannot be recovered reliably by post-hoc text-only grapheme-to-phoneme conversion. We present Ruby-ASR, which refines the conventional target into a span-bound orthographic--lexical-reading sequence. Unlike separate full-sentence orthographic and phonological outputs, the ruby representation locally binds each written span to its realized reading and permits deterministic recovery of both views. We instantiate the target under subtitle-style and verbatim-style transcription conventions using a Qwen3-ASR backbone; a mora-level CTC objective provides auxiliary monotonic reading supervision. The experimental results across five Japanese benchmarks show that refining the recognition target can improve lexical-reading recovery without sacrificing readable orthographic transcription. We release the checkpoints and inference code.

Problem

• Automatic Speech Recognition
• Orthographic Transcript
• Lexical Reading
• Supervision

Innovation

• Ruby-ASR
• Orthographic-Lexical-Reading Sequence
• Evidence-Preserving Supervision
• Mora-Level CTC Objective