Perceptual Adaptation to Device and Human Voices: Learning and Generalization of a Phonetic Shift Across Real and Voice-AI Talkers

Segedin, Bruno Ferenc; Cohn, Michelle; Zellou, Georgia

doi:10.21437/interspeech.2019-1433

Interspeech 2019 2019

DOI: 10.21437/interspeech.2019-1433

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Perceptual Adaptation to Device and Human Voices: Learning and Generalization of a Phonetic Shift Across Real and Voice-AI Talkers

Bruno Ferenc Segedin

Michelle Cohn

Georgia Zellou

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Cited by 3 publications

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“…As a final note, the fact that our stimuli were generated by TTS confirms the basic feasibility of perceptual learning from TTS voices and extends prior work that had conflated guise with acoustic differences (Ferenc Segedin et al 2019).…”

supporting

confidence: 77%

Listener beliefs and perceptual learning: Differences between device and human guises

Zellou,

Cohn,

Pycha

2023

lan

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Listeners have a remarkable ability to adapt to novel speech patterns, such as a new accent or an idiosyncratic pronunciation. In almost all of the previous studies examining this phenomenon, the participating listeners had reason to believe that the speech signal was produced by a human being. However, people are increasingly interacting with voice-activated artificially intelligent (voice-AI) devices that produce speech using text-to-speech (TTS) synthesis. Will listeners also adapt to novel speech input when they believe it is produced by a device? Across three experiments, we investigate this question by exposing American English listeners to shifted pronunciations accompanied by either a ‘human’ or a ‘device’ guise and testing how this exposure affects their subsequent categorization of vowels. Our results show that listeners exhibit perceptual learning even when they believe the speaker is a device. Furthermore, listeners generalize these adjustments to new talkers, and do so particularly strongly when they believe that both old and new talkers are devices. These results have implications for models of speech perception, theories of human-computer interaction, and the interface between social cognition and linguistic theory.

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supporting

confidence: 77%

Listener beliefs and perceptual learning: Differences between device and human guises

Zellou,

Cohn,

Pycha

2023

lan

View full text Add to dashboard Cite

show abstract

supporting

confidence: 77%

Listener beliefs and perceptual learning: Differences between device and human guises

Zellou,

Cohn,

Pycha

2023

lan

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Partial compensation for coarticulatory vowel nasalization across concatenative and neural text-to-speech

Zellou

Cohn

Block

2021

The Journal of the Acoustical Society of America

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This study investigates the perception of coarticulatory vowel nasality generated using different text-to-speech (TTS) methods in American English. Experiment 1 compared concatenative and neural TTS using a 4IAX task, where listeners discriminated between a word pair containing either both oral or nasalized vowels and a word pair containing one oral and one nasalized vowel. Vowels occurred either in identical or alternating consonant contexts across pairs to reveal perceptual sensitivity and compensatory behavior, respectively. For identical contexts, listeners were better at discriminating between oral and nasalized vowels in neural than in concatenative TTS for nasalized same-vowel trials, but better discrimination for concatenative TTS was observed for oral same-vowel trials. Meanwhile, listeners displayed less compensation for coarticulation in neural than in concatenative TTS. To determine whether apparent roboticity of the TTS voice shapes vowel discrimination and compensation patterns, a “roboticized” version of neural TTS was generated (monotonized f0 and addition of an echo), holding phonetic nasality constant; a ratings study (experiment 2) confirmed that the manipulation resulted in different apparent roboticity. Experiment 3 compared the discrimination of unmodified neural TTS and roboticized neural TTS: listeners displayed lower accuracy in identical contexts for roboticized relative to unmodified neural TTS, yet the performances in alternating contexts were similar.

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Perceptual Adaptation to Device and Human Voices: Learning and Generalization of a Phonetic Shift Across Real and Voice-AI Talkers

Cited by 3 publications

References 13 publications

Listener beliefs and perceptual learning: Differences between device and human guises

Listener beliefs and perceptual learning: Differences between device and human guises

Listener beliefs and perceptual learning: Differences between device and human guises

Partial compensation for coarticulatory vowel nasalization across concatenative and neural text-to-speech

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