用音高特征自动识别和聚类汉语方言声调，提升田野调查效率。
Automated Tone Transcription and Clustering with Tone2Vec
- 基于音高构建声调相似性表示Tone2Vec，捕捉细微声调差异。
- 在方言聚类任务中表现优异，准确率显著优于传统方法。
- 开源工具ToneLab支持自动化声调分析，适合语言学家与跨区域研究者。
声调在汉藏语系语言中至关重要，但当前语音田野工作高度依赖人工，耗时耗资，尤其对濒危语言而言更是雪上加霜。本文提出一种基于音高的声调相似性表示方法——Tone2Vec，实验表明其能有效捕捉精细的声调变异。基于Tone2Vec，我们首次实现了声调转写与聚类的全自动方法，提出了新颖的转写表示转换机制。相关算法已系统集成至开源易用的工具包ToneLab，支持自动化田野工作及跨区域、跨词汇的语言分析。大量实验验证了该方法的有效性。
原文摘要 · Abstract (English)
Lexical tones play a crucial role in Sino-Tibetan languages. However, current phonetic fieldwork relies on manual effort, resulting in substantial time and financial costs. This is especially challenging for the numerous endangered languages that are rapidly disappearing, often compounded by limited funding. In this paper, we introduce pitch-based similarity representations for tone transcription, named Tone2Vec. Experiments on dialect clustering and variance show that Tone2Vec effectively captures fine-grained tone variation. Utilizing Tone2Vec, we develop the first automatic approach for tone transcription and clustering by presenting a novel representation transformation for transcriptions. Additionally, these algorithms are systematically integrated into an open-sourced and easy-to-use package, ToneLab, which facilitates automated fieldwork and cross-regional, cross-lexical analysis for tonal languages. Extensive experiments were conducted to demonstrate the effectiveness of our methods.
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