Dual-Form ASR: Semantics-Aware Inverse Text Normalization for Chinese Speech Recognition
Modern automatic speech recognition (ASR) scenarios require both spoken-form transcripts for faithful transcription and readable written-form transcripts with inverse text normalization (ITN). However, these forms are typically produced by cascaded modules, where a spoken-form ASR output is rewritten by a separate ITN component, making written-form ASR-ITN vulnerable to recognition errors and decoupling normalization from acoustic-contextual modeling, especially for semantically dependent numeric expressions. In this paper, we propose Dual-Form ASR (DF-ASR), a framework that extends spoken-form ASR capability to semantics-aware written-form ITN through paired spoken-form and written-form ...
| Authors | Fengrun Zhang, Li Fu, Wangjin Zhou et al. |
|---|---|
| Primary category | cs.CL |
| Published | 6 July 2026 |
| Last updated at source | 6 July 2026 |
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