Persuasive Synthetic Speech: Voice Perception and User Behaviour
Open accessAbstract
Previous research indicates that synthetic speech can be as persuasive as human speech. However, there is a lack of empirical validation on interactive goal-oriented tasks. In our two-stage study (online listening test and lab evaluation), we compared participants' perception of the persuasiveness of synthetic voices created from speech in a debating style vs. speech from audio-books. Participants interacted with our Conversational Agent (CA) to complete 4 flight-booking tasks and were asked to evaluate the voice, message and perceived personal qualities. We found that participants who interacted with the CA using the voice created from debating style speech rated it as significantly more truthful and more involved than the CA using the audio-book-based voice. However, there was no difference in how frequently each group followed the CA's recommendations. We hope our investigation will provoke discussion about the impact of different synthetic voices on users' perceptions of CAs in goal-oriented tasks.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Prior research such as Dubiel et al. has indicated that even minor variations in voice characteristics can significantly influence user perceptions.
“Given that prior research, such as Dubiel et al. [26, 27], has indicated that even minor variations in voice characteristics can significantly influence user perceptions, we decided to adopt a synthetic voice for the user role as well.”
From Examining Humanness as a Metaphor to Design Voice User Interfaces (2024) · cites this paper as [26]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
1 retracted
Schirmer, A., Feng, Y., Sen, A., & Penney, T. B. (2019) · PLoS ONE · doi:10.1371/journal.pone.0210555 · Cited by 28 · see citing works
RetractedOn a Test of Whether one of Two Random Variables is Stochastically Larger than the Other
Mann, H. B., & Whitney, D. R. (1947) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177730491 · Cited by 14,320 · see citing works
Baddeley, A. (1992) · Science · doi:10.1126/science.1736359 · Cited by 5,258 · see citing works
WaveNet: a generative model for raw audio
Oord, A. V. D., Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A., Kalchbrenner, N., Senior, A., & Kavukcuoglu, K. (2016) · arXiv · doi:10.48550/arxiv.1609.03499 · Cited by 4,911 · see citing works
LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speech
Zen, H., Dang, V., Clark, R., Zhang, Y., Weiss, R. J., Ye, J., Chen, Z., & Wu, Y. (2019) · Interspeech (USB) · doi:10.21437/interspeech.2019-2441 · Cited by 828 · see citing works
Springer Handbook of Speech Processing
Benesty, J., Sondhi, M. M., & Huang, Y. A. (2007) · Springer handbooks · doi:10.1007/978-3-540-49127-9 · Cited by 729 · see citing works
Wizard of Oz studies — why and how
Dahlbäck, N., Jönsson, A., & Ahrenberg, L. (1993) · Knowledge-Based Systems · doi:10.1016/0950-7051(93)90017-n · Cited by 706 · see citing works
Taylor, P. (2009) · Cambridge University Press eBooks · doi:10.1017/cbo9780511816338 · Cited by 549 · see citing works
Scherer, K. R., Johnstone, & Klasmeyer, G. (2002) · Handbook of Affective Sciences · doi:10.1093/oso/9780195126013.003.0023 · Cited by 426 · see citing works
Feinberg, D. R., Jones, B. C., Little, A. C., Burt, D. M., & Perrett, D. I. (2004) · Animal Behaviour · doi:10.1016/j.anbehav.2004.06.012 · Cited by 423 · see citing works
How Do You Say ‘Hello’? Personality Impressions from Brief Novel Voices
McAleer, P., Todorov, A. T., & Belin, P. (2014) · PLoS ONE · doi:10.1371/journal.pone.0090779 · Cited by 392 · see citing works
Complete Counterbalancing of Immediate Sequential Effects in a Latin Square Design
Bradley, J. V. (1958) · Journal of the American Statistical Association · doi:10.1080/01621459.1958.10501456 · Cited by 343 · see citing works
Tachibana, H., Uenoyama, K., & Aihara, S. (2018) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2018.8461829 · Cited by 277 · see citing works
The Role of Femininity and Averageness of Voice Pitch in Aesthetic Judgments of Women's Voices
Feinberg, D. R., DeBruine, L. M., Jones, B. C., et al. (2008) · Perception · doi:10.1068/p5514 · Cited by 209 · see citing works
The Development and Validation of the Rational and Intuitive Decision Styles Scale
Hamilton, K., Shih, S.-I., & Mohammed, S. (2016) · Journal of Personality Assessment · doi:10.1080/00223891.2015.1132426 · Cited by 187 · see citing works
Processing Rational and Emotional Messages: The Cognitive and Affective Mediation of Persuasion
Rosselli, F., Skelly, J. J., & Mackie, D. M. (1995) · Journal of Experimental Social Psychology · doi:10.1006/jesp.1995.1008 · Cited by 139 · see citing works
Affective Information Processing
Tao, J., & Tan, T. (2008) · Springer eBooks · doi:10.1007/978-1-84800-306-4 · Cited by 106 · see citing works
Yamagishi, J., Veaux, C., King, S., & Renals, S. J. (2012) · Nippon Onkyo Gakkaishi/Acoustical science and technology/Nihon Onkyo Gakkaishi · doi:10.1250/ast.33.1 · Cited by 93 · see citing works
Self-citationThe sound of trustworthiness: Acoustic-based modulation of perceived voice personality
Belin, P., Boehme, B., & McAleer, P. (2017) · PLoS ONE · doi:10.1371/journal.pone.0185651 · Cited by 85 · see citing works
Exploring the Role of Conversational Cues in Guided Task Support with Virtual Assistants
Vtyurina, A., & Fourney, A. (2018) · Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3173782 · Cited by 83 · see citing works
Siri On-Device Deep Learning-Guided Unit Selection Text-to-Speech System
Capes, T., Coles, P., Conkie, A. D., Golipour, L., Hadjitarkhani, A., Hu, Q., Huddleston, N. F., Hunt, M. J., Li, J., Neeracher, M., et al. (2017) · Interspeech (USB) · doi:10.21437/interspeech.2017-1798 · Cited by 62 · see citing works
Evaluating information presentation strategies for spoken recommendations
Winterboer, A., & Moore, J. D. (2007) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/1297231.1297260 · Cited by 58 · see citing works
Voice and Persuasion in a Banking Telemarketing Context
Chebat, J., Hedhli, K. E., Gélinas‐Chebat, C., & Boivin, R. (2007) · Perceptual and Motor Skills · doi:10.2466/pms.104.2.419-437 · Cited by 55 · see citing works
The Persuasiveness of Synthetic Speech versus Human Speech
Stern, S. E., Mullennix, J. W., Dyson, C.-L., & Wilson, S. J. (1999) · Human Factors The Journal of the Human Factors and Ergonomics Society · doi:10.1518/001872099779656680 · Cited by 54 · see citing works
Festival 2 - build your own general purpose unit selection speech synthesiser.
Clark, R. A., Richmond, K., & King, S. (2004) · ERA · Cited by 52 · see citing works
Self-citationStern, S. E., Mullennix, J. W., & Yaroslavsky, I. (2005) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2005.07.002 · Cited by 52 · see citing works
Tate, M. W., & Brown, S. M. (1970) · Journal of the American Statistical Association · doi:10.1080/01621459.1970.10481069 · Cited by 51 · see citing works
Wester, M., Valentini-Botinhao, C., & Henter, G. E. (2015) · Interspeech (USB) · doi:10.21437/interspeech.2015-689 · Cited by 41 · see citing works
Integrating fundamental and formant frequencies in women’s preferences for men’s voices
Feinberg, D. R., Jones, B. C., DeBruine, L. M., et al. (2011) · Behavioral Ecology · doi:10.1093/beheco/arr134 · Cited by 34 · see citing works
Attributes of a telemarketer's voice and persuasiveness
Ketrow, S. M. (1990) · Journal of Direct Marketing · doi:10.1002/dir.4000040304 · Cited by 29 · see citing works
Beyond the Listening Test: An Interactive Approach to TTS Evaluation
Mendelson, J., & Aylett, M. (2017) · Interspeech · doi:10.21437/interspeech.2017-1438 · Cited by 27 · see citing works
Proc. Blizzard Challenge Workshop 2006
Aylett, M. P., Pidcock, C. J., & Fraser, M. E. (2006) · Cited by 25 · see citing works
LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speech
Zen, H., Dang, V., Clark, R., Zhang, Y., Weiss, R. J., Jia, Y., Chen, Z., & Wu, Y. (2019) · arXiv · doi:10.48550/arxiv.1904.02882 · Cited by 23 · see citing works
Speech intonation for TTS: study on evaluation methodology
Latorre, J., Yanagisawa, K., Wan, V., et al. (2014) · Interspeech (USB) · doi:10.21437/interspeech.2014-204 · Cited by 12 · see citing works
Stern, S. E., & Mullennix, J. W. (2004) · Psychological Reports · doi:10.2466/pr0.94.3c.1283-1292 · Cited by 8 · see citing works
Towards Human-Like Conversational Search Systems
Dubiel, M. (2018) · Conference on Human Information Interaction and Retrieval · doi:10.1145/3176349.3176360 · Cited by 6 · see citing works
Self-citationGoogle's new voice is as good as your own
Kobie, N. (2018) · The New Scientist · doi:10.1016/s0262-4079(18)30010-1 · Cited by 4 · see citing works
Improving speaker skill in a resynthesis experiment
Strangert, E., & Gustafson, J. (2008) · Cited by 2 · see citing works
Rogers, J., Clarke, L., Skelly, M., Taylor, N., Thomas, P., Thorne, M., Larsen, S., Odrozek, K., Kloiber, J., Bihr, P., et al. (2019) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300344 · Cited by 2 · see citing works
Flagged first, then by citations
Research works printed but not linked: 9
- Mateusz Dubiel, Martin Halvey, Leif Azzopardi, and Sylvain Daronnat. 2018. Investigating how conversational search agents affect user's behaviour, perfor- mance and search experience. In The Second International Workshop on Conversa- tional Approaches to Information Retrieval. https://tinyurl.com/sigir-cair
- Kerstin Fischer. 2004. Expressive Speech Characteristics in the Communication with Artificial Agents. In Proceedings of the AISB 2004 Convention. The Society for the Study of Artificial Intelligence and the Simulation of Behaviour, 1-11. https://tinyurl.com/communicating-artificialagents
- Vincent Gaudissart, Silvio Ferreira, Céline Thillou, and Bernard Gosselin. 2004. SYPOLE: mobile reading assistant for blind people. In 9th Conference Speech and Computer.
- Andrew Gibiansky, Sercan Arik, Gregory Diamos, John Miller, Kainan Peng, Wei Ping, Jonathan Raiman, and Yanqi Zhou. 2017. Deep voice 2: Multi-speaker neural text-to-speech. In Advances in neural information processing systems. 2962-2970.
- Thomas F Gordon. 1993. The pleadings game. Artificial Intelligence and Law 2, 4 (1993), 239-292.
- Shachar Mirkin, Michal Jacovi, Tamar Lavee, Hong-Kwang Kuo, Samuel Thomas, Leslie Sager, Lili Kotlerman, Elad Venezian, and Noam Slonim. 2017. A Recorded Debating Dataset. arXiv preprint arXiv:1709.06438 (2017). · arXiv
- Eva Strangert and Joakim Gustafson. 2008. What makes a good speaker? subject ratings, acoustic measurements and perceptual evaluations. In Ninth Annual Conference of the International Speech Communication Association.
- Tangming Yuan, David Moore, and Alec Grierson. 2008. A human-computer dialogue system for educational debate: A computational dialectics approach. International Journal of Artificial Intelligence in Education 18, 1 (2008), 3-26.
- Heiga Zen, Keiichi Tokuda, and Alan W Black. 2009. Statistical parametric speech synthesis. speech communication 51, 11 (2009), 1039-1064.
Cited by
Sorted by citations
Examining Humanness as a Metaphor to Design Voice User Interfaces
Desai, S., Dubiel, M., & Leiva, L. A. (2024) · ACM Conversational User Interfaces 2024 · doi:10.1145/3640794.3665535 · Cited by 21 · see citing works
Self-citationImpact of Voice Fidelity on Decision Making: A Potential Dark Pattern?
Dubiel, M., Sergeeva, A., & Leiva, L. A. (2024) · Proceedings of the 29th International Conference on Intelligent User Interfaces · doi:10.1145/3640543.3645202 · Cited by 16 · see citing works
Self-citationWei, C. Z., Kim, Y.-H., & Kuzminykh, A. (2023) · Proceedings of the 5th International Conference on Conversational User Interfaces · doi:10.1145/3571884.3597139 · Cited by 14 · see citing works
- Authors
- Dubiel, M., Halvey, M., Gallegos, P. O., & King, S.
- Affiliations
- University of Strathclyde (GB)
- University of Edinburgh (GB)
- Venue
- Proceedings of the 2nd Conference on Conversational User Interfaces
- Publisher
- ACM
- Published
- 2020-07-22
- Pages
- 1-9
- OpenAlex
- W3042754020
- Cited by
- 28
- References
- 39
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 9 · 2023: 3 · 2022: 6 · 2021: 4 · 2020: 1
- FWCI
- 1.34
- Type
- conference paper
- Language
- en
- Open access
- Locations
- ACM Conference on Conversational User Interfaces (CUI) (publishedVersion)
- Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, other-oa)
- Strathprints: The University of Strathclyde institutional repository (University of Strathclyde) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in Service Interactions
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Keywords
- synthetic speech, persuasive speech, chatbots, speech synthesis, goal-oriented interaction, perceived truthfulness, user engagement, airline reservations
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review