Publications

Abstract

When speaking to infants, adults often produce speech that differs systematically from that directed to other adults. In order to quantify the acoustic properties of this speech style across a wide variety of languages and cultures, we extracted results from empirical studies on the acoustic features of infant-directed speech (IDS). We analyzed data from 88 unique studies (734 effect sizes) on the following five acoustic parameters that have been systematically examined in the literature: i) fundamental frequency (fo), ii) fo variability, iii) vowel space area, iv) articulation rate, and v) vowel duration. Moderator analyses were conducted in hierarchical Bayesian robust regression models in order to examine how these features change with infant age and differ across languages, experimental tasks and recording environments. The moderator analyses indicated that fo, articulation rate, and vowel duration became more similar to adult-directed speech (ADS) over time, whereas fo variability and vowel space area exhibited stability throughout development. These results point the way for future research to disentangle different accounts of the functions and learnability of IDS by conducting theory-driven comparisons among different languages and using computational models to formulate testable predictions.

Abstract

In the present paper we investigate the effect of categorising raw behavioural data or computational model responses. In addition, the effect of averaging over stimuli from potentially different populations is assessed. To this end, we replicate studies on word learning and generalisation abilities using the ACORNS models. Our results show that discrete categories may obscure interesting phenomena in the continuous responses. For example, the finding that learning in the model saturates very early at a uniform high recognition accuracy only holds for categorical representations. Additionally, a large difference in the accuracy for individual words is obscured by averaging over all stimuli. Because different words behaved differently for different speakers, we could not identify a phonetic basis for the differences. Implications and new predictions for infant behaviour are discussed.

Abstract

In the present paper we investigate the effect of categorising raw behavioural data or computational model responses. In addition, the effect of averaging over stimuli from potentially different populations is assessed. To this end, we replicate studies on word learning and generalisation abilities using the ACORNS models. Our results show that discrete
categories may obscure interesting phenomena in the continuous
responses. For example, the finding that learning in the model saturates very early at a uniform high recognition accuracy only holds for categorical representations. Additionally, a large difference in the accuracy for individual words is obscured
by averaging over all stimuli. Because different words behaved
differently for different speakers, we could not identify a phonetic
basis for the differences. Implications and new predictions for
infant behaviour are discussed.