arXiv Machine Learning
Oct 12

Revisiting Accent Classification with DistilHuBERT: Speaker-Disjoint Evaluation and Cross-Corpus Generalization

The paper evaluates the robustness of DistilHuBERT for accent classification using speaker‑disjoint splits and cross‑corpus tests with L2‑ARCTIC and other English accent datasets. It finds a large performance gap between in‑corpus and external evaluations, indicating that file‑level tests overestimate accuracy. The study shows that balanced external‑data training performs best, while pseudo‑labeling with confidence filtering is insufficient under domain mismatch.

By Xu He, Homayoon Beigi
arXiv Machine Learning
Aug 28

Benchmarking_Fast_Domain_Adaptation_for_Unsupervised_Speech_Units

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
Hugging Face Trending Papers
Aug 27

Benchmarking_Fast_Domain_Adaptation_for_Unsupervised_Speech_Units

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.

arXiv Computation and Language
Oct 15

CAFE: Confining Feature Distillation to Language-Routed Adapters for Compact Multilingual Automatic Speech Recognition

The paper introduces CAFE, a method that confines Centered Kernel Alignment (CKA) feature distillation to language‑routed adapters in compact multilingual ASR models. By restricting the CKA gradient to adapters that English bypasses, CAFE mitigates the trade‑off where CKA improves non‑English WER at the cost of English performance. On the Open ASR Leaderboard, CAFE reduces average WER from 8.55% to 8.02% and outperforms an adapter‑only capacity control, achieving competitive results across 48 test sets in 24 languages with 254M parameters.

By Tianchi Liu, Nicholas Moratelli, Khurram Azeem Hashmi, Mohammadreza Zolfaghari, Rishabh Jain, Louis Lu, Changdae Park, Amril Nazir