Preschoolers rely on rich speech representations to process variable speech

Abstract

To process language in real time, listeners must map a highly variable speech signal to linguistic categories such as phones and words. How do children learn to process this rampant variation and distinguish between variable pronunciations of known words ("shoup" for soup) versus novel words ("cheem") in their environments? By examining the unique sensory experiences of children with cochlear implants, we show that successful speech processing relies upon access to fine phonetic detail, beginning in the early preschool years. In a mispronunciation sensitivity eye-tracking task, we matched children with cochlear implants, who have poorer-resolution electric hearing, to their peers with typical acoustic hearing, controlling for (1) lifetime oral language exposure (hearing-age matches) and (2) language skill (vocabulary size matches). Online processing of familiar words (soup) depended on vocabulary, replicating previous work, but processing of variable pronunciations ("shoup") depended on access to fine-grained phonetic detail. These results suggest that children of all hearing experiences use their lexical knowledge to process familiar words, but they rely on fine phonetic detail and well-specified phonological representations to process variable speech in real time.

Strategic Pauses Relieve Listeners from the Effort of Listening to Fast Speech: Data Limited and Resource Limited Processes in Narrative Recall by Adult Users of Cochlear Implants

“…As such, the time points where the shaded regions do not overlap with the bold line at the 0 point on the y -axis represent a moment in the time series where the difference between conditions is statistically significant. We follow Cychosz et al (2023) in showing the significant regions of the time series in red and the nonsignificant regions in blue using the tidymv package (Coretta, 2022).…”

The effort of repairing a misperceived word can impair perception of following words, especially for listeners with cochlear implants

“…Pupil data were modeled using generalized additive mixed-effects models (GAMMs), which have been used in previous studies of time-series changes in pupil dilation (Van Rij et al 2019;Poretta et al 2019;Pandža et al 2020), and eye-tracking data (Cychosz et al 2023). GAMMs are similar to traditional generalized linear models, but the linear predictor partly depends on smoothing functions consisting of a combination of Gaussian (or other) basis functions that combine to form the nonlinear shape of the modeled data.…”