My work pictured by AI – Volker Dellwo

What is this work about? Voice timbre – the unique acoustic information in a voice by which its speaker can be recognized – is particularly critical in mother-infant interaction. Vocal timbre is necessary for infants to recognize their mothers as familiar both before and after birth, providing a basis for social bonding between infant and mother. The exact mechanisms underlying infant voice recognition are unknown. Here, we show – for the first time – that mothers’ vocalizations contain more detail of their vocal timbre through adjustments to their voices known as infant-directed speech (IDS) or baby talk, resulting in utterances in which individual recognition is more robust. Using acoustic modelling (k-means clustering of Mel Frequency Cepstral Coefficients) of IDS in comparison with adult-directed speech (ADS), we found across a variety of languages from different cultures that voice timbre clusters in IDS are significantly larger to comparable clusters in ADS. This effect leads to a more detailed representation of timbre in IDS with subsequent benefits for recognition. Critically, an automatic speaker identification Gaussian-mixture model based on Mel Frequency Cepstral Coefficients showed significantly better performance when trained with IDS as opposed to ADS. We argue that IDS has evolved as part of a set of adaptive evolutionary strategies that serve to promote indexical signalling by caregivers to their offspring which thereby promote social bonding via voice and acquiring language.

Comment about the picture from the author? The study is about ‘voice recognition’ and the advantage that infant-directed speech offers in learning a voice. I am not sure someone would conclude this from looking at the pictures.