arXiv:2609.14231v1 Announce Type: cross
Abstract: Controllable synthesis of nonverbal vocalizations (NVVs) is es- sential for natural and expressive speech, but remains challeng- ing due to their aco...
By Ziyu Zhang, Yun Chen, Taihui Wang, Hanzhao Li, Qicong Xie, Rilin Chen, Zhixian Zhao, Lei Xie
The paper introduces ABX-Accent, a benchmark built on the AESRC dataset that evaluates how well representation learning models adapt to 10 different English accents with less than 10 hours of unlabeled data per accent. It adapts the Zero Resources Challenge ABX metrics for each accent and demonstrates a baseline using adaptive domain normalization to fine‑tune a Contrastive Predictive Coding model, achieving a 23.6% relative improvement on across‑speaker ABX scores compared to non‑adapted models. The dataset and evaluation metrics will be released publicly after the paper is accepted.
By Robin San Roman, Manel Khentout, Tu Anh Nguyen, Paul Michel, Yossi Adi, Emmanuel Dupoux
arXiv:2607. 12468v1 Announce Type: cross Abstract: We describe our submission to Task 1 of the 2nd MLCSLM Challenge: a cascaded diarization-then-recognition system that combines DiariZen-Large-s80 (WavLM-Large) segmentation, CAM++ embedding-based two-speaker clustering, and a LoRA-adapted omniASR LLM 7B v2 recognizer, with no oracle segmentation or speaker labels at test time.
By Shuming Fang, Shuifei Zeng
The paper introduces ABX-Accent, a benchmark for evaluating how well unsupervised speech representation learning models adapt to new accents. It uses the AESRC dataset with 10 English accents, each providing less than 10 hours of unlabeled data, and adapts the Zero Resources Challenge ABX metrics to these accents. A baseline model employing adaptive domain normalization to fine‑tune a Contrastive Predictive Coding model shows a 23.6% relative improvement on across‑speaker ABX scores compared to non‑adapted models.
DiscoPhon is a multilingual benchmark designed to evaluate unsupervised phoneme discovery from discrete speech units. It includes 6 development and 6 test languages that cover a wide range of phonemic contrasts, and requires systems to generate discrete units mapped to a predefined phoneme inventory using only 10 hours of speech from an unseen language. The benchmark assesses unit quality, recognition, and segmentation, and provides four pretrained multilingual HuBERT and SpidR baselines that demonstrate current models can produce units that correlate well with phonemes, though performance varies across languages.
By Maxime Poli, Manel Khentout, Angelo Ortiz Tandazo, Ewan Dunbar, Emmanuel Chemla, Emmanuel Dupoux
The paper introduces asymmetric classifier‑free guidance (CFG) for target‑speaker ASR using Whisper, where a speaker‑conditioned branch predicts the target transcript and a speaker‑unconditioned branch predicts serialized multi‑speaker transcripts. CFG modulates the influence of speaker conditioning during decoding via a single guidance scale, which is first set globally on development data and then refined per utterance by a lightweight encoder‑based predictor while keeping the recognition model fixed. The resulting system yields up to 21.8% relative WER reduction over a condition‑only baseline and 5.6% over standard conditional decoding under domain shifts.
By Yiwen Guan, Jacob Whitehill