Interdisciplinary Corpus-based Approach for Exploring Multimodal Conversational Feedback

Boudin, Auriane

doi:10.1145/3536221.3557029

Cited by 2 publications

(1 citation statement)

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“…The results from the user evaluation through human computer interaction show that the model trained on speaker behavioral cues offers both smoother turn-transitions and more responsive system behavior. Other research that concentrated on feedback position during conversation identified feedback locations through multimodal models ( Boudin et al, 2021 ), Interdisciplinary Corpus ( Boudin, 2022 ), and voice activity ( Truong et al, 2010 ; Ekstedt and Skantze, 2022 ).…”

Section: Literature Reviewmentioning

confidence: 99%

Talk like me: Exploring the feedback speech rate regulation strategy of the voice user interface for elderly people

Wang

Yang

2023

Front. Psychol.

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Voice user interface (VUI) is widely used in developing intelligent products due to its low learning cost. However, most of such products do not consider the cognitive and language ability of elderly people, which leads to low interaction efficiency, poor user experience, and unfriendliness to them. Firstly, the paper analyzes the factors which influence the voice interaction behavior of elderly people: speech rate of elderly people, dialog task type, and feedback word count. And then, the voice interaction simulation experiment was designed based on the wizard of Oz testing method. Thirty subjects (M = 61.86 years old, SD = 7.16; 15 males and 15 females) were invited to interact with the prototype of a voice robot through three kinds of dialog tasks and six configurations of the feedback speech rate. Elderly people’s speech rates at which they speak to a person and to a voice robot, the feedback speech rates they expected for three dialog tasks were collected. The correlation between subjects’ speech rate and the expected feedback speech rate, the influence of dialog task type, and feedback word count on elderly people’s expected feedback speech rate were analyzed. The results show that elderly people speak to a voice robot with a lower speech rate than they speak to a person, and they expected the robot feedback speech rate to be lower than the rate they speak to the robot. There is a positive correlation between subjects’ speech rate and the expected speech rate, which implies that elderly people with faster speech rates expected a faster feedback speech rate. There is no significant difference between the elderly people’s expected speech rate for non-goal-oriented and goal-oriented dialog tasks. Meanwhile, a negative correlation between the feedback word count and the expected feedback speech rate is found. This study extends the knowledge boundaries of VUI design by investigating the influencing factors of voice interaction between elderly people and VUI. These results also provide practical implications for developing suitable VUI for elderly people, especially for regulating the feedback speech rate of VUI.

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