Speech Rate Adjustments in Conversations With an Amazon Alexa Socialbot

Cohn, Michelle; Liang, Kai-Hui; Sarian, Melina; Zellou, Georgia; Yu, Zhou

doi:10.3389/fcomm.2021.671429

Cited by 19 publications

(4 citation statements)

References 43 publications

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“…In the present study, speakers showed a systematic Alexa-DS speech style: when talking to Alexa, speakers produced sentences with a slower rate, higher mean f0, and higher f0 variation, relative to human-DS. These differences align with prior work showing slowed speech rate toward Alexa socialbot (Cohn et al, 2021), increased higher mean f0 in speech toward voice-AI (Raveh et al, 2019), and greater segmental lengthening in computer-DS (Burnham et al, 2010). Furthermore, both an increased mean f0 and f0 variation are consistent with increased vocal effort in response to a presumed communicative barrier; for instance, prior work has reported that speakers produce greater f0 variation in response to a word FIGURE 4 | Mean acoustic changes from speakers' citation form productions to the interaction with the Interlocutors (Alexa vs. human) for vowel duration (milliseconds, ms), F1 (log Hertz, Hz), and F2 (log Hertz, Hz).…”

Section: Discussionsupporting

confidence: 88%

Prosodic Differences in Human- and Alexa-Directed Speech, but Similar Local Intelligibility Adjustments

Cohn

Zellou

2021

Front. Commun.

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The current study tests whether individuals (n = 53) produce distinct speech adaptations during pre-scripted spoken interactions with a voice-AI assistant (Amazon’s Alexa) relative to those with a human interlocutor. Interactions crossed intelligibility pressures (staged word misrecognitions) and emotionality (hyper-expressive interjections) as conversation-internal factors that might influence participants’ intelligibility adjustments in Alexa- and human-directed speech (DS). Overall, we find speech style differences: Alexa-DS has a decreased speech rate, higher mean f0, and greater f0 variation than human-DS. In speech produced toward both interlocutors, adjustments in response to misrecognition were similar: participants produced more distinct vowel backing (enhancing the contrast between the target word and misrecognition) in target words and louder, slower, higher mean f0, and higher f0 variation at the sentence-level. No differences were observed in human- and Alexa-DS following displays of emotional expressiveness by the interlocutors. Expressiveness, furthermore, did not mediate intelligibility adjustments in response to a misrecognition. Taken together, these findings support proposals that speakers presume voice-AI has a “communicative barrier” (relative to human interlocutors), but that speakers adapt to conversational-internal factors of intelligibility similarly in human- and Alexa-DS. This work contributes to our understanding of human-computer interaction, as well as theories of speech style adaptation.

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Section: Discussionsupporting

confidence: 88%

Prosodic Differences in Human- and Alexa-Directed Speech, but Similar Local Intelligibility Adjustments

Cohn

Zellou

2021

Front. Commun.

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“…We do not observe register-level differences in speech rate in the present study, as has been observed in related work (e.g., Cohn et al, 2022; but see Cohn, Liang, et al, 2021;. Here, speakers produce similar speech rate adjustments for voice-AI and human addressees.…”

Section: Production and Perception Of Device-directed Speechsupporting

confidence: 87%

Prosodic focus marking for voice-AI and human addressees

Beier,

Cohn,

Trammel

et al. 2023

Preprint

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Prosodic prominence (through increased pitch, intensity, duration) is thought to guide listeners’ attention to new information, which relies on the assumption that a rational listener will benefit from these prosodic cues. This study investigates production and perception of prosodic focus marking toward two types of addresses: a human and a voice assistant interlocutor, a potentially less-than-rational listener. Stimuli consisted of question-answer pairs, where American English speakers read identical sentences (e.g., “Jude saw the sun”) in response to interlocutors’ questions probing different foci (e.g., “Who saw the sun?”). Experiment 1 reveals consistent acoustic adjustments to mark focus on either the subject or object of a sentence: speakers increase vowel intensity and duration. In Experiment 2, we find that listeners reliably infer the intended information structure based on these acoustic adjustments. Across both experiments, we see no consistent difference in focus marking by type of interlocutor (human vs. voice assistant). However, listeners associate particular features (e.g., slower speech rate) with speech directed at voice assistants. Taken together, our findings suggest that while speakers apply communicative strategies from human-human interaction when addressing voice assistants, listeners expect a device-specific register.

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“…A higher pitch has only been reported for two other studies for device-DS, one in German (voice assistant) 19 and one in French (robot) 23 . Duration increases (or decreased speech rate) is a more commonly reported feature of technology-DS for adults (e.g., for a computer avatar 10 or imagined computer 44 , or Alexa socialbot 16 , or social robot 21 ). In the current study, adults and children made both duration and pitch adjustments, supporting routinized interaction theories of human–computer interaction 43 , in which people have distinct modes of engaging with technology than with other humans.…”

Section: Discussionmentioning

confidence: 99%

Children and adults produce distinct technology- and human-directed speech

Cohn,

Barreda,

Graf Estes

et al. 2024

Sci Rep

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This study compares how English-speaking adults and children from the United States adapt their speech when talking to a real person and a smart speaker (Amazon Alexa) in a psycholinguistic experiment. Overall, participants produced more effortful speech when talking to a device (longer duration and higher pitch). These differences also varied by age: children produced even higher pitch in device-directed speech, suggesting a stronger expectation to be misunderstood by the system. In support of this, we see that after a staged recognition error by the device, children increased pitch even more. Furthermore, both adults and children displayed the same degree of variation in their responses for whether “Alexa seems like a real person or not”, further indicating that children’s conceptualization of the system’s competence shaped their register adjustments, rather than an increased anthropomorphism response. This work speaks to models on the mechanisms underlying speech production, and human–computer interaction frameworks, providing support for routinized theories of spoken interaction with technology.

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Speech Rate Adjustments in Conversations With an Amazon Alexa Socialbot

Cited by 19 publications

References 43 publications

Prosodic Differences in Human- and Alexa-Directed Speech, but Similar Local Intelligibility Adjustments

Prosodic Differences in Human- and Alexa-Directed Speech, but Similar Local Intelligibility Adjustments

Prosodic focus marking for voice-AI and human addressees

Children and adults produce distinct technology- and human-directed speech

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