Influence of TTS Systems Performance on Reaction Times in People with Aphasia
Source abstract
Abstract
Text-to-speech (TTS) systems provide fundamental reading support for people with aphasia and reading difficulties. However, artificial voices are more difficult to process than natural voices. The current study is an extended analysis of the results of a clinical experiment investigating which, among three artificial voices and a digitised human voice, is more suitable for people with aphasia and reading impairments. Such results show that the voice synthesised with Ogmios TTS, a concatenative speech synthesis system, caused significantly slower reaction times than the other three voices used in the experiment. The present study explores whether and what voice quality metrics are linked to delayed reaction times. For this purpose, the voices were analysed using an automatic assessment of intelligibility, naturalness, and jitter and shimmer voice quality parameters. This analysis revealed that Ogmios TTS, in general, performed worse than the other voices in all parameters. These observations could explain the significantly delayed reaction times in people with aphasia and reading impairments when listening to Ogmios TTS and could open up consideration about which TTS to choose for compensative devices for these patients based on the voice analysis of these parameters.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Speech Recognition and Synthesis · Speech and dialogue systems · EEG and Brain-Computer Interfaces
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
TALN Research Group, Universitat Pompeu Fabra, 08017 Barcelona, Spain
Erasmus MC, Department of Rehabilitation Medicine, University Medical Center Rotterdam, 3015 GD Rotterdam, The Netherlands
Rijndam Rehabilitation, 3015 LJ Rotterdam, The Netherlands
Language and Computation Centre, Universitat de Barcelona, 08007 Barcelona, Spain
References (28)
Show source-provided references
1. Comprehension of Single Versus Combined Modality Information by People With Aphasia10.1044/2018_AJSLP-17-0132 ↗
2. Effects of Written, Auditory, and Combined Modalities on Comprehension by People With Aphasia10.1044/2019_AJSLP-19-0013 ↗
3. Narrative Comprehension by People with Aphasia given Single versus Combined Modality Presentation10.1080/02687038.2018.1506088 ↗
4. Aphasia and Acquired Reading Impairments: What Are the High-Tech Alternatives to Compensate for Reading Deficits?10.1111/1460-6984.12569 ↗
5. Comprehension of Synthetic Speech by Individuals with Aphasia
6. Comprehension of Synthetic Speech and Digitized Natural Speech by Adults with Aphasia10.1016/j.jcomdis.2017.06.006 ↗
7. Toward the Integration of Resource Allocation into a General Theory of Aphasia
8. Attention and Aphasia: Theory, Research and Clinical Implications10.1080/026870399402226 ↗
9. Perceptions of People with Aphasia about Supporting Reading with Text-to-Speech Technology: A Convergent Mixed Methods Study10.1016/j.jcomdis.2021.106098 ↗
10. DMDX: A Windows Display Program with Millisecond Accuracy10.3758/BF03195503 ↗
11. Taylor, P., Black, A.W., and Caley, R. (1998, January 26–29). The Architecture of the Festival Speech Synthesis System. Proceedings of the 3rd ESCA Workshop on Speech Synthesis, Jenolan Caves House, Blue Mountains Australia.10.1049/ic:19980957 ↗
12. Bonafonte, A., Agüero, P.D., Adell, J., Pérez, J., and Moreno, A. (2006, January 19–21). Ogmios: The UPC Text-to-Speech Synthesis System for Spoken Translation. Proceedings of the TC-Star Workshop on Speech-to-Speech Translation, Barcelona, Spain.
13. Baby, A., Vinnaitherthan, S., Adiga, N., Jawale, P., Badam, S., Adavanne, S., and Konjeti, S. (2020). An ASR Guided Speech Intelligibility Measure for TTS Model Selection. arXiv.
14. Binary Codes Capable of Correcting Deletions, Insertions, and Reversals
15. Pratap, V., Hannun, A., Xu, Q., Cai, J., Kahn, J., Synnaeve, G., Liptchinsky, V., and Collobert, R. (2019, January 12–17). Wav2Letter++: A Fast Open-Source Speech Recognition System. Proceedings of the ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing—Proceedings 2019, Brighton, UK.10.1109/ICASSP.2019.8683535 ↗
16. Pratap, V., Xu, Q., Sriram, A., Synnaeve, G., and Collobert, R. (2020, January 25–29). MLS: A Large-Scale Multilingual Dataset for Speech Research. Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH 2020, Shanghai, China.10.21437/Interspeech.2020-2826 ↗
17. Suppes, P. (1968). Naturalness of Synthesized Speech. University-Level Computer-Assisted Instruction at Stanford: 1968–1980, Insititute for Mathematical Studies in the Social Sciences, Stanford University Publisher.
18. Soni, M.H., and Patil, H. (2016, January 13–15). Non-Intrusive Quality Assessment of Synthesized Speech Using Spectral Features and Support Vector Regression. Proceedings of the 9th ISCA Speech Synthesis Workshop, Sunnyvale, CA, USA.10.21437/SSW.2016-21 ↗
19. Mittag, G., and Möller, S. (2020, January 25–29). Deep Learning Based Assessment of Synthetic Speech Naturalness. Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH 2020, Shanghai, China.10.21437/Interspeech.2020-2382 ↗
20. Farrús, M., Hernando, J., and Ejarque, P. (2007, January 27–31). Jitter and Shimmer Measurements for Speaker Recognition Mireia. Proceedings of the INTERSPEECH 2007, 8th Annual Conference of the International Speech Communication Association, Antwerp, Belgium.10.21437/Interspeech.2007-147 ↗
21. Vocal Acoustic Analysis—Jitter, Shimmer and HNR Parameters10.1016/j.protcy.2013.12.124 ↗
22. Digital Signal Processing in the Differential Diagnosis of Benign Larynx Diseases [Abstract in English]
23. Accuracy of Jitter and Shimmer Measurements10.1016/j.protcy.2014.10.134 ↗
24. Slyh, R.E., Nelson, W.T., and Hansen, E.G. (1999, January 15–19). Analysis of Mrate, Shimmer, Jitter, and F/Sub 0/ Contour Features across Stress and Speaking Style in the SUSAS Database. Proceedings of the 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258), Phoenix, AZ, USA.10.1109/ICASSP.1999.758345 ↗
25. PRAAT, a System for Doing Phonetics by Computer
26. Ziegler, A. (2011). Generalized Estimating Equations, Springer Science & Business Media.10.1007/978-1-4614-0499-6 ↗
27. An Introduction to Statistical Parametric Speech Synthesis
28. Cohn, M., and Zellou, G. (2020, January 25–29). Perception of Concatenative vs. Neural Text-to-Speech (TTS): Differences in Intelligibility in Noise and Language Attitudes. Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH 2020, Shanghai, China.10.21437/Interspeech.2020-1336 ↗