Extreme stop allophony in Mixtec spontaneous speech: Data, word prosody, and modelling. (May 2022) - DiCanio, Christian; Chen, Wei-Rong; Benn, Joshua; Amith, Jonathan D.; Castillo García, Rey
Highlights: We examine lenition of obstruents in a spontaneous speech corpus of Yoloxóchitl Mixtec (ISO xty), a language with fixed stem-final stress and a complex tonal inventory. Measures of duration, voicing during constriction, and allophone type were examined. Onset obstruents of unstressed syllables are lenited more than onset obstruents of stem-final stressed syllables. Duration is a strong predictor of the degree of lenition, but the functional status of the morpheme is also important. Modeling of the reduced allophones with deep neural networks resulted in high accuracy in the detection of stop closure (>95%), and fairly high (>70%) accuracy in detecting highly frequent reduced allophones. Abstract: 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.