Word-level prosody plays an important role in processes of consonant lenition. Typically, consonants in word-initial position are strengthened while those in word-medial position are lenited (Keating, Cho, Fougeron, & Hsu, 2003). In this paper we examine the relationship between word-prosodic position and obstruent lenition in a spontaneous speech corpus of Yoloxóchitl Mixtec, an endangered Mixtecan language spoken in Mexico. The language exhibits a surprising amount of lenition in the realization of otherwise voiceless unaspirated stops and voiceless fricatives in careful speech. In Experiment 1, we examine the relationships between word position, consonant duration, and passive voicing and find that word-medial pre-tonic position is the locus of both consonant lengthening and less passive voicing. Non-pre-tonic consonants are produced with more voicing and shorter duration. We also find that the functional status of the morpheme plays a role in voicing lenition. In Experiment 2, we examine manner lenition and find a similar pattern – word-medial pre-tonic stops are more often realized with complete closure relative to non-pre-tonic stops, which are more often realized with incomplete closure. In Experiment 3, we model these lenition patterns using a series of deep neural networks and find that, even with limited training data, we can achieve reasonably high accuracy in the automatic categorization of lenition patterns. The results of this research both complement recent work on the phonetics of lenition in the world’s languages (Katz and Fricke, 2018; White et al., 2020) and provide computational tools for modeling and predicting patterns of extreme lenition.

词级韵律在辅音朗读过程中起着重要作用。通常，词首位置的辅音会被加强，而词中位置的辅音会被削弱（Keating、Cho、Fougeron 和 Hsu，2003）。在本文中，我们研究了 Yoloxóchitl Mixtec（墨西哥使用的一种濒临灭绝的 Mixtecan 语言）的自发语音语料库中的单词韵律位置和阻碍性放宽之间的关系。该语言在实现其他清不送气停音和仔细讲话中的清摩擦音方面表现出惊人的宽容度。在实验1中，我们检查了单词位置、辅音持续时间和被动发声之间的关系，发现单词中间的前主音位置是辅音延长和较少被动发声的场所。非前强辅音的发音较多，持续时间较短。我们还发现语素的功能状态在语音放宽中发挥着作用。在实验 2 中，我们检查了方式延展并发现了类似的模式——相对于非前主音停音，单词中间的前主音停音更经常以完全闭合的方式实现，而非前主音停音更经常以不完全闭合的方式实现。在实验 3 中，我们使用一系列深度神经网络对这些学习模式进行建模，并发现即使训练数据有限，我们也可以在学习模式的自动分类中实现相当高的准确度。这项研究的结果既补充了最近关于世界语言缓释语音学的研究（Katz 和 Fricke，2018；White 等人，2020），又提供了用于建模和预测极端缓释模式的计算工具。