When talking with their young infants, parents instinctively use "baby talk," a unique form of speech including exaggerated pitch contours and short, repetitive phrases.
"We use timbre, the tone colour or unique quality of a sound, all the time to distinguish people, animals, and instruments," said Elise Piazza from Princeton University in the US.
"We found that mothers alter this basic quality of their voices when speaking to infants, and they do so in a highly consistent way across many diverse languages," said Piazza.
The researchers recorded 12 English-speaking mothers while they played with and read to their 7- to 12-month-old infants. They also recorded those mothers while they spoke to another adult.
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After quantifying each mother's unique vocal fingerprint using a concise measure of timbre, the researchers found that a computer could reliably tell the difference between infant- and adult-directed speech.
Using an approach called machine learning, the researchers found that a computer could learn to differentiate baby talk from normal speech based on just one second of speech data.
The next question was whether those differences would hold true in mothers speaking other languages.
The researchers enlisted another group of 12 mothers who spoke nine different languages, including Spanish, Russian, Polish, Hungarian, German, French, Hebrew, Mandarin, and Cantonese.
They found that the timbre shift observed in English- speaking mothers was highly consistent across those languages from around the world.
"The machine learning algorithm, when trained on English data alone, could immediately distinguish adult-directed from infant-directed speech in a test set of non-English recordings and vice versa when trained on non-English data, showing strong generalisability of this effect across languages," Piazza said.
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