This paper reports an automatic data-driven analysis for describing prototypical intonation patterns, particularly suitable for initial stages of prosodic research and language description. The approach has several advantages over traditional ways to investigate intonation, such as the applicability to spontaneous speech, language- and domain-independency, and the potential of revealing meaningful functions of intonation. These features make the approach particularly useful for language documentation, where the description of prosody is often lacking. The core of this approach is a cluster analysis on a time-series of f0 measurements and consists of two scripts (Praat and R, available from https://constantijnkaland.github.io/contourclustering/). Graphical user interfaces can be used to perform the analyses on collected data ranging from spontaneous to highly controlled speech. There is limited need for manual annotation prior to analysis and speaker variability can be accounted for. After cluster analysis, Praat textgrids can be generated with the cluster number annotated for each individual contour. Although further confirmatory analysis is still required, the outcomes provide useful and unbiased directions for any investigation of prototypical f0 contours based on their acoustic form.

本文报告了一种用于描述原型语调模式的自动数据驱动分析，特别适用于韵律研究和语言描述的初始阶段。与研究语调的传统方法相比，该方法有几个优点，例如对自发语音的适用性、语言和领域的独立性，以及揭示语调有意义功能的潜力。这些特性使该方法对语言文档特别有用，其中通常缺乏对韵律的描述。这种方法的核心是对 f0 测量时间序列的聚类分析，由两个脚本（Praat 和 R，可从 https://constantijnkaland.github.io/contourclustering/ 获得）组成。图形用户界面可用于对收集的数据进行分析，范围从自发到高度受控的语音。在分析之前对手动注释的需求有限，并且可以考虑说话者的可变性。聚类分析后，可以生成 Praat 文本网格，其中为每个单独的轮廓注释了聚类编号。尽管仍需要进一步的验证性分析，但结果为基于其声学形式的原型 f0 轮廓的任何调查提供了有用且公正的方向。