Social Feedback Speech Technologies (SFST) are programs and devices, often "AI"-powered, that claim to provide users with feedback about how their speech sounds to other humans. To date, academic research has not focused on how such systems perform for a variety of speakers. In 2020, Amazon released a wearable called Halo, touting its fitness and sleep tracking, as well as its ability to evaluate the wearer's voice to help them "understand how they sound to others".
View Article and Find Full Text PDFThis study tests the effects of intonational contours and filtering conditions on listener judgments of ethnicity to arrive at a more comprehensive understanding on how prosody influences these judgments, with implications for austomatic speech recognition systems as well as speech synthesis. In a perceptual experiment, 40 American English listeners heard phrase-long clips which were controlled for pitch accent type and focus marking. Each clip contained either two H* (high) or two L+H* (low high) pitch accents and a L-L% (falling) boundary tone, and had also previously been labelled for broad or narrow focus.
View Article and Find Full Text PDFWiley Interdiscip Rev Cogn Sci
January 2019
What is the relationship between ethnolinguistic communities and ways of speaking? Who is an authentic speaker of an ethnolinguistic variety? In a time where scholarly and public conceptualizations of race and ethnicity are variable and rapidly changing, potential effects on both self-identification and ways of speaking present an area ripe for study. However, linguistics and allied fields have often overlooked individuals and communities that do not neatly conform to well-defined racial categories. As multiracially identified individuals are one of the fastest growing demographic groups in the United States, researchers will necessarily need to address the way that traditional methodologies have excluded individuals and groups who fall outside of these racial and ethnic categories.
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