My work pictured by AI – Sarah Saneei

What is this work about? Vocal learning plays an important role during speech development in human infants and is vital for language. However, the complex structure of language creates a colossal challenge in quantifying and tracking vocal changes in humans. Consequently, animals with simpler vocal communication systems are powerful tools for understanding the mechanisms underlying vocal learning. While human infants show the most drastic vocal changes, many adult animals, including humans, continue to show vocal learning in the form of a much-understudied phenomena called vocal accommodation. Vocal accommodation is often seen when people use similar words, pronunciations and speech rate to their conversing partner. Such a phenomena is also seen in common marmosets, a highly voluble Brazilian monkey species, with a simpler communication system compared to humans. In this project, I developed a mathematical model that explains the basic principles and rules underlying marmoset vocal accommodation. The model provides crucial insights into the mechanisms underlying vocal learning in adult animals and how they might differ from vocal learning in infant animals and humans.

The first word that came to mind when seeing the AI-generated picture? Monkey-learning.

What is this work about? The relationship between speech rhythmicity and neural oscillations is an important component of speech perception, and especially of comprehension. However, even though the presence of the same rhythm has been described in non-human primates, and neural oscillations are a basic property of animals’ brains, we still do not know how the brain of animals is processing rhythmic information. Therefore, by identifying similarities and differences in rhythm, as well as its connection with brain oscillations in animal species, we hope to uncover the common rules that govern the rhythmic production and processing of vocal signals in animals. These results will help us understand how speech fits or detached itself from these basic rules, giving us new insight into the evolution of language complex hierarchical structure and a better understanding of brains’ perception mechanisms of vocal signals.

The first word that came to mind when seeing the AI-generated picture? /

What is this work about? Today Transformer language models dominate the natural language processing domain. In our work, we introduce a new perspective on these models, which in turn provide new emerging capabilities!

The first word that came to mind when seeing the AI-generated picture? Superhero!

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.