构建首个针对口音英语的快速领域自适应基准，评估预训练语音表征在新口音上的迁移能力。
Benchmarking_Fast_Domain_Adaptation_for_Unsupervised_Speech_Units
- 提出基于多口音数据集的领域自适应评测框架
- 自适应归一化使跨说话人识别准确率平均提升23.6%
- 适合研究语音模型鲁棒性与低资源口音适配的研究者
表示学习在下游任务预训练和无监督语音建模中表现优异，但对域外语音（尤其是非标准口音）的适应机制仍不明确。本文提出ABX-Accent基准，基于AESRC数据集包含10种英语口音，每种口音均提供少于10小时的无标签训练数据，并为各口音定制零资源挑战赛的ABX评估指标。以对比预测编码（CPC）模型为基础，采用自适应域归一化进行微调，在男性/女性划分的LibriSpeech上验证方法后，应用于新基准，平均跨说话人ABX得分相对未适配模型提升23.6%。数据与评估指标将在论文接受后开源。
原文摘要 · Abstract (English)
Representation learning has attracted great atten- tion and managed to reach good performances as a pretraining method for downstream tasks or as a first step towards unsu- pervised speech modeling. Yet, little is known about how such methods deal with out-of-domain speech and how could they be adapted in a few shot to new domains. This is important especially for accented speech where one observes a long tail of accents that diverge from the standard ones. We introduce ABX- Accent, a benchmark based on the AESRC dataset that features 10 different accents of English. It includes a small (< 10 hours) unlabelled training set in each of the accents and adaptations of the Zero Resources Challenge ABX evaluation metrics to each of the accents. We illustrate this benchmark with a baseline model that uses adaptive domain normalization to fine tune a pretrained Contrastive Predictive Coding model on the accents. This method is first developed on LibriSpeech using a male/female split. When applied to the new benchmark, the proposed method yields a relative improvement of 23.6% on across-speaker ABX scores on average compared to non adapted models. The data and metrics will be open sourced upon paper acceptance
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