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

arXiv:2609.27289 · cs.CL, cs.AI · Submitted 2026-09-23 · Read on arXiv

cs.CL, cs.AI

Submitted: 2026-09-23

Updated: 2026-09-23

Code: https://github.com/hshi-speech/Ruby-ASR-1.7B

Terminology

• Accuracy — 8× in this paper · explained in 1 episode
• Benchmark — 8× in this paper · explained in 2 episodes
• Corpus of Spontaneous Japanese — 2× in this paper
• Regularization — 2× in this paper · explained in 3 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• Automatic Speech Recognition (ASR) — 1× in this paper · explained in 2 episodes
• Benchmarking — 1× in this paper · explained in 3 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Hallucination — 1× in this paper · explained in 3 episodes
• Knowledge Distillation — 1× in this paper · explained in 11 episodes

Sources

• Sarashina2.2-TTS: Tackling Kanji Polyphony in Japanese Speech Generation via Data Scaling and Targeted Data Synthesis
• Distilling LLM Semantic Priors into Encoder-Only Multi-Talker ASR with Talker-Count Routing
• JSUT corpus: free large-scale Japanese speech corpus for end-to-end speech synthesis
• Benchmarking Large Language Models for Grapheme-to-Phoneme Conversion: A Japanese Case Study
• YOMI-Bench: A Benchmark for Evaluating Kanji Reading and Phonological Understanding of LLMs for Japanese
• Distil-Whisper: Robust Knowledge Distillation via Large-Scale Pseudo Labelling
• A Dual-Decoder Conformer for Multilingual Speech Recognition
• Qwen3-ASR Technical Report

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