Extreme stop allophony in Mixtec spontaneous speech: data, word prosody, and modelling.

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 et al., 2003). In this paper we examine the relationship between wordprosodic 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.

Using automatic alignment to analyze endangered language data: testing the viability of untrained alignment.

While efforts to document endangered languages have steadily increased, the phonetic analysis of endangered language data remains a challenge. The transcription of large documentation corpora is, by itself, a tremendous feat. Yet, the process of segmentation remains a bottleneck for research with data of this kind. This paper examines whether a speech processing tool, forced alignment, can facilitate the segmentation task for small data sets, even when the target language differs from the training language. The authors also examined whether a phone set with contextualization outperforms a more general one. The accuracy of two forced aligners trained on English (hmalign and p2fa) was assessed using corpus data from Yoloxóchitl Mixtec. Overall, agreement performance was relatively good, with accuracy at 70.9% within 30 ms for hmalign and 65.7% within 30 ms for p2fa. Segmental and tonal categories influenced accuracy as well. For instance, additional stop allophones in hmalign's phone set aided alignment accuracy. Agreement differences between aligners also corresponded closely with the types of data on which the aligners were trained. Overall, using existing alignment systems was found to have potential for making phonetic analysis of small corpora more efficient, with more allophonic phone sets providing better agreement than general ones.