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.
In Taiwanese Hokkien automatic speech recognition (ASR), prior studies often treat tone sandhi as a major challenge under the assumption that models fail to process implicit phonological variations. H...
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...
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.
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...
arXiv:2609.38658v1 Announce Type: cross Abstract: TTS systems with autoregressive semantic modeling have demonstrated strong zero-shot voice cloning performance and rich expressive variation, but the...
arXiv:2609.14991v1 Announce Type: new Abstract: Open Thai automatic speech recognition (ASR) is dominated by offline, Whisper-based models that read the whole utterance before transcribing, ruling ou...
The paper presents a method for creating a compact fixed‑voice Thai text‑to‑speech system by training a student model on synthetic speech generated from a large voice‑cloning teacher. By using only a short 15‑second voice reference and carefully filtering synthetic data, the authors build an 82‑million‑parameter model, Wayu‑Paxa‑TTS‑Edge, that runs on device without reference audio. The system achieves strong performance—68.2 % challenge‑set keyword accuracy, 91.4 % pause precision, and low character error rates—while outperforming its teacher and approaching the quality of a larger Gemini 3.1 model.
arXiv:2607. 02633v1 Announce Type: new Abstract: We present GRAFT, a per-word pronunciation conditioning mechanism for text-to-speech neural codec language modeling.
Conversational ASR for lower-resource languages and niche domains is limited by the scarcity of domain-matched multi-speaker training data. We propose an augmentation pipeline that generates scenario-level dialogues with participant metadata, maps speaker attributes to TTS voice profiles, and assembles synthesized utterances into speaker-aware simulated conversations.
VietPrism is a newly released, large‑scale Vietnamese speech corpus that combines 993.4 hours of real utterances from 1,262 verified speakers with 3.1 k hours of synthetic spoof speech. It uniquely offers transcripts, consistent speaker identities, five dialect groups, and extensive Vietnamese‑English code‑switching—nearly half of the corpus—while pairing each spoof with a matched bona fide utterance. The dataset enables controlled evaluation of deep‑fake detection models, revealing significant variability in detector performance across dialects and speaker similarity.
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:2606. 03957v1 Announce Type: cross Abstract: Conversational ASR for lower-resource languages and niche domains is limited by the scarcity of domain-matched multi-speaker training data.