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T‑SANDHI is a lightweight Taiwanese Hokkien ASR model that addresses tone sandhi by decoupling surface acoustics from lexical intent on a frozen Whisper backbone. It uses a lexicon‑guided multi‑task learning framework with text‑derived pseudo labels and a hybrid injection module that dynamically gates independent citation and sandhi phonetic streams. Experiments on the TAT‑MOE corpus and two blind test sets show that this explicit disentanglement resolves tonal mapping confusion and outperforms baselines while keeping parameter usage low.
arXiv:2608.29239v1 Announce Type: new Abstract: Low-resource ASR remains difficult because scarce transcripts provide limited supervised evidence for target-side generation. To address this gap, we p...
arXiv:2601.09050v2 Announce Type: replace Abstract: Tonal low-resource languages are widely spoken but remain underserved by modern speech technologies. A central challenge is learning speech represe...
Recent advances in zero-shot text-to-speech (TTS) have substantially improved speech quality and voice cloning fidelity. However, many zero-shot TTS systems still depend on audio prompt transcripts at inference time.
Text-to-speech systems increasingly process user-generated text (UGT) such as ppl and imo, whose pronunciation must be inferred from the canonical rather than surface form. We introduce UGTPhon, the f...
arXiv:2607. 02633v1 Announce Type: new Abstract: We present GRAFT, a per-word pronunciation conditioning mechanism for text-to-speech neural codec language modeling.