仅用声学标签提取巴葡方言特征，效果优于通用模型。
Extracting accent features in spoken Brazilian Portuguese without sociolinguistic labels
- 基于音素强制对齐提取局部方言特征
- 在无社会语言标签下实现更优的方言区分性能
- 适合语音识别与方言分析研究者使用
巴西葡萄牙语（pt-BR）的区域口音分类面临可靠标注不足的问题。尽管大规模自监督学习（SSL）语音模型能力强大，但其训练流程会稀释社会语音信息，因口音标签通常不可靠或未用于训练目标。本文提出一种新工作流，仅使用声学标签进行特征提取。通过分离明确的区域口音标志，并采用基于音素的强制对齐工具（ZIPA），所生成的目标特征集能更有效地捕捉方言差异，相比语句嵌入表现更优。结果表明，在仅使用少量且客观的声学标签条件下，局部特征可超越通用架构在口音相关任务上的表现。
原文摘要 · Abstract (English)
Regional accent classification in Brazilian Portuguese (pt-BR) suffers from the need for reliable labeling. While large self-supervised learning (SSL) speech models are powerful, their training pipelines dilute sociophonetic information, since accent labels are generally not reliable or are not used in training objectives. This work introduces a novel workflow for feature extraction using only acoustic labels. By isolating explicit regional accent landmarks and using a phoneme-based forced aligner (ZIPA), our targeted feature set captures dialectal variance more effectively than utterance embeddings, demonstrating that localized features can outperform general-purpose architectures on accent-related tasks using minimal and objective data labels.
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