arXiv:2606. 29031v1 Announce Type: cross Abstract: In regulated domains such as banking and healthcare, where privacy constraints make real speech costly to collect and retain, synthetic speech from modern text-to-speech (TTS) is an appealing alternative for training automatic speech recognition (ASR) without exposing sensitive customer recordings.
By Yanis Labrak, Dairazalia Sanchez-Cortes, Sergio Burdisso, S\'everin Baroudi, Shashi Kumar, Esa\'u Villatoro-Tello, Srikanth Madikeri, Manjunath K E, Old\v{r}ich Plchot, Kadri Hacio\u{g}lu, Petr Motlicek, Andreas Stolcke
arXiv:2609.26536v1 Announce Type: new
Abstract: In LLM-based speech translation, transcription-based chain-of-thought (CoT) suffers from a mismatch between reference transcripts used in supervised fi...
By Yanghe Dong, Wanting Huang, Weiran Wang
arXiv:2610.32572v1 Announce Type: new
Abstract: Adapting ASR to a domain with only untranscribed audio typically relies on pseudo-labeling, a strong teacher transcribes the audio and a student is fin...
By Shashi Kumar, Yanis Labrak, Sergio Burdisso, Esa\'u Villatoro-Tello, Petr Motlicek, Kadri Hacioglu, Andreas Stolcke
The paper introduces a new benchmark of 13,044 spontaneous Nepali‑English code‑switched podcast utterances, totaling 15.45 h, and evaluates a two‑stage synthetic‑to‑real adaptation framework for ASR. Stage 1 uses supervised fine‑tuning on 0.95 h of synthetic speech, reducing Mixed Error Rate from 23.40 % to 21.52 % and switch‑point error rate from 35.89 % to 28.77 %. Stage 2 applies Group Relative Policy Optimization with script and transcription rewards, yielding modest but statistically significant further improvements, particularly in English script confusion and switch‑point accuracy. The study also shows that an auxiliary script reward term offers no benefit beyond MER‑only optimization in this setting, and that the synthetic SFT provides the main cross‑domain transfer to spontaneous human speech.
By Firoj Paudel
SyRA-CS is a synthetic-to-real adaptation framework for code‑switched ASR that uses Reptile meta‑learning to create pair‑specific initializations from synthetic CS speech. For each language pair and pretrained model, the system meta‑trains on a synthetic CS‑FLEURS subset and then adapts with just one hour of labeled real target speech. Across four architectural settings and four language pairs, SyRA-CS consistently outperforms continual fine‑tuning, achieving up to 17.7% relative WER reduction for XLS‑R and 5.3–5.8% for other models.
By Fadel Thior, Ahmer Tabassum, Haania Siddiqui, Hanan Aldarmaki, Thamar Solorio
The paper introduces Speech-Rewarded Style Planning (SRSP), a method that trains a text-based style planner using a frozen downstream TTS model. SRSP generates candidate style instructions from dialogue history and response text, optimizing them with group-relative policy optimization (GRPO) where the reward is the teacher‑forced likelihood of target speech tokens. Experiments on an English subset of the ISCSLP 2026 CoT‑TTS corpus show that SRSP outperforms baseline LLM and target‑audio‑informed captioning approaches in speech‑style and emotion similarity, mel‑cepstral distortion, and contextual appropriateness.
"whyItMatters":"The study demonstrates a more effective way to align natural‑language style descriptions with actual acoustic output, improving controllability and fidelity in conversational text‑to‑speech systems."
By Shiao Zhu, Lianbo Liu, Sizhen Lyu, Yuzhe Wang, Sheng Li, Takahiro Shinozaki