Our meetings this quarter will be held on Zoom. Please sign up for the listserv to receive the Zoom link (instructions in sidebar). On December 16, Dr. Xin Xie will be joining us to present:

Navigating speech variability via distributional learning: what is there to learn?

One of the central unresolved questions in speech perception is how listeners overcome talker-to-talker variability in the meaning-to-sound mapping. In addition to low-level domain-general normalization processes, speech-specific normalization (e.g., McMurray & Jongman, 2011), storage (e.g., Goldinger, 1996; Johnson, 1997), and distributional learning (e.g., Clayards et al., 2008; Kleinschmidt & Jaeger, 2015) have been proposed as a mechanism to navigate this problem. However, these views have typically been investigated separately from one another, with different phonetic/phonological contrasts. As a result, existing evidence is often compatible with multiple accounts.

In this talk, I present a step towards a stronger test of these competing accounts, jointly against the same dataset. This approach combines: 1) production experiments to estimate within-/across-talker variability in the acoustic cue distributions, 2) computational modeling to quantify the expected amount of information listeners can gain from learning the talker-level versus group-level distributions, and 3) perception experiments to probe if distributional learning indeed predicts changes in listeners’ categorization judgments. To demonstrate, I focus on a study including a database of prosodic productions (65 talkers, ~3000 tokens), with which we directly tested whether learning of phonetic cue distributions (normalized or not) can in principle afford listeners the means to navigate the variability in prosodic perception.