Analyzing the relationships between pretraining language, phonetic, tonal, and speaker information in self-supervised speech models

Analyses of self-supervised speech models have begun to reveal where and how they represent different types of information. However, almost all analyses have focused on English. Here, we examine how wav2vec2 models trained on four different languages encode both language-matched and non-matched speech. We use probing classifiers and geometric analyses to examine how phones, lexical tones, and speaker information are represented. We show that for all pretraining and test languages, the subspaces encoding phones, tones, and speakers are largely orthogonal, and that layerwise patterns of probing accuracy are similar, with a relatively small advantage for matched-language phone and tone (but not speaker) probes in the later layers. Our findings suggest that the structure of representations learned by wav2vec2 is largely independent of the speech material used during pretraining.

Actuation without production bias

Phonetic production bias is the external force most commonly invoked in computational models of sound change, despite the fact that it is not responsible for all, or even most, sound changes. Furthermore, the existence of production bias alone cannot account for how changes do or do not propagate throughout a speech community. While many other factors have been invoked by (socio)phoneticians, including but not limited to contact (between subpopulations) and differences in social evaluation (of variants, groups, or individuals), these are not typically modeled in computational simulations of sound change. In this paper, we consider whether production biases have a unique dynamics in terms of how they impact the population-level spread of change in a setting where agents learn from multiple teachers. We show that, while the dynamics conditioned by production bias are not unique, it is not the case that all perturbing forces have the same dynamics: in particular, if social weight is a function of individual teachers and the correlation between a teacher's social weight and the extent to which they realize a production bias is weak, change is unlikely to propagate. Nevertheless, it remains the case that changes initiated from different sources may display a similar dynamics. A more nuanced understanding of how population structure interacts with individual biases can thus provide a (partial) solution to the `non-phonologization problem'.

Bias and population structure in the actuation of sound change

Why do human languages change at some times, and not others? We address this longstanding question from a computational perspective, focusing on the case of sound change. Sound change arises from the pronunciation variability ubiquitous in every speech community, but most such variability does not lead to change. Hence, an adequate model must allow for stability as well as change. Existing theories of sound change tend to emphasize factors at the level of individual learners promoting one outcome or the other, such as channel bias (which favors change) or inductive bias (which favors stability). Here, we consider how the interaction of these biases can lead to both stability and change in a population setting. We find that population structure itself can act as a source of stability, but that both stability and change are possible only when both types of bias are active, suggesting that it is possible to understand why sound change occurs at some times and not others as the population-level result of the interplay between forces promoting each outcome in individual speakers. In addition, if it is assumed that learners learn from two or more teachers, the transition from stability to change is marked by a phase transition, consistent with the abrupt transitions seen in many empirical cases of sound change. The predictions of multiple-teacher models thus match empirical cases of sound change better than the predictions of single-teacher models, underscoring the importance of modeling language change in a population setting.