Beyond Words: Measuring User Experience through Speech Analysis in Voice User Interfaces
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866914411185176576 |
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| author | Ma, Yong Zhang, Xuesong Zhang, Xuedong Bartłomiejczyk, Natalia Je, Seungwoo Holzer, Adrian Fjeld, Morten Butz, Andreas |
| author_facet | Ma, Yong Zhang, Xuesong Zhang, Xuedong Bartłomiejczyk, Natalia Je, Seungwoo Holzer, Adrian Fjeld, Morten Butz, Andreas |
| contents | Voice assistants (VAs) are typically evaluated through task performance metrics and self-report questionnaires, but people's voices themselves carry rich paralinguistic cues that reveal affect, effort, and interaction breakdowns. We present a within-subjects study (N=49) that systematically compared three VA personas across three usage scenarios to investigate whether speech-derived audio features can serve as a proxy for user experience (UX). Participants' speech was analyzed for temporal, spectral, and linguistic markers, alongside standardized UX measures, brief mood and stress ratings, and a post-study questionnaire. We found correlations between specific speech features and self-reported satisfaction and experience. Furthermore, a machine learning model trained on speech features achieved promising accuracy in classifying UX levels, indicating that this might be a reasonable alternative to self-report instruments. Our findings establish speech as a viable, real-time signal for implicitly measuring UX and point toward adaptive VUIs that respond dynamically to emotional and usability-related vocal cues. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_19904 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Beyond Words: Measuring User Experience through Speech Analysis in Voice User Interfaces Ma, Yong Zhang, Xuesong Zhang, Xuedong Bartłomiejczyk, Natalia Je, Seungwoo Holzer, Adrian Fjeld, Morten Butz, Andreas Human-Computer Interaction Voice assistants (VAs) are typically evaluated through task performance metrics and self-report questionnaires, but people's voices themselves carry rich paralinguistic cues that reveal affect, effort, and interaction breakdowns. We present a within-subjects study (N=49) that systematically compared three VA personas across three usage scenarios to investigate whether speech-derived audio features can serve as a proxy for user experience (UX). Participants' speech was analyzed for temporal, spectral, and linguistic markers, alongside standardized UX measures, brief mood and stress ratings, and a post-study questionnaire. We found correlations between specific speech features and self-reported satisfaction and experience. Furthermore, a machine learning model trained on speech features achieved promising accuracy in classifying UX levels, indicating that this might be a reasonable alternative to self-report instruments. Our findings establish speech as a viable, real-time signal for implicitly measuring UX and point toward adaptive VUIs that respond dynamically to emotional and usability-related vocal cues. |
| title | Beyond Words: Measuring User Experience through Speech Analysis in Voice User Interfaces |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2603.19904 |