We are pleased to share a new publication from Ivy, Frank and Youngah in the Journal of Experimental Psychology: Learning, Memory, and Cognition. The paper, titled “Modeling the impact of prenatal audio attenuation on speech sound learning,” examines how human infants appear to have substantial knowledge of the sound structure of their native language at birth, despite the fact that the uterine environment strongly limits auditory input to low-frequency sounds.
The study explores whether this prenatal low-frequency exposure may actually support later speech sound learning rather than hinder it. To address this question, the authors trained neural network models in two stages designed to simulate prenatal and postnatal learning. During the prenatal stage, models were exposed to speech that was either naturally low-pass filtered, artificially high-pass filtered, or unfiltered. After birth, all models were trained on full-frequency speech. Three different neural network architectures were examined, including a long short-term memory network, a convolutional neural network, and a residual neural network, to test whether the effects generalised across learning systems.
Across architectures, the results showed that prenatal exposure to low-frequency speech led to faster and more effective phonetic learning once full-frequency input became available. In contrast, exposure to high-frequency–only speech was less beneficial during prenatal learning. These findings suggest that the low-frequency sounds available before birth may provide a useful foundation that helps infants extrapolate to the richer speech input they encounter after birth, offering a computational explanation for early speech sound knowledge.
Zheng, S., Tan, F. & Do, Y. (2026). Modeling the impact of prenatal audio attenuation on speech sound learning. Journal of Experimental Psychology: Learning, Memory, and Cognition. Advance online publication. open_in_newDOI
