Contribution of Vocal Tract and Glottal Source Spectral Cues in the Generation of Acted Happy and Aggressive Spanish Vowels
Source abstract
Abstract
The source-filter model is one of the main techniques applied to speech analysis and synthesis. Recent advances in voice production by means of three-dimensional (3D) source-filter models have overcome several limitations of classic one-dimensional techniques. Despite the development of preliminary attempts to improve the expressiveness of 3D-generated voices, they are still far from achieving realistic results. Towards this goal, this work analyses the contribution of both the the vocal tract (VT) and the glottal source spectral (GSS) cues in the generation of happy and aggressive speech through a GlottDNN-based analysis-by-synthesis methodology. Paired neutral expressive utterances are parameterised to generate different combinations of expressive vowels, applying the target expressive GSS and/or VT cues on the neutral vowels after transplanting the expressive prosody on these utterances. The conducted objective tests focused on Spanish [a], [i] and [u] vowels show that both GSS and VT cues significantly reduce the spectral distance to the expressive target. The results from the perceptual test show that VT cues make a statistically significant contribution in the expression of happy and aggressive emotions for [a] vowels, while the GSS contribution is significant in [i] and [u] vowels.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Speech Recognition and Synthesis · Phonetics and Phonology Research · Speech and Audio Processing
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
GTM—Grup de Recerca en Tecnologies Mèdia, La Salle—Universitat Ramon Llull, Sant Joan de la Salle, 42, 08022 Barcelona, Spain
Funding
Agencia Estatal de Investigación
References (48)
Show source-provided references
1. A Review on Five Recent and Near-Future Developments in Computational Processing of Emotion in the Human Voice10.1177/1754073919898526 ↗
2. A Comprehensive Review of Speech Emotion Recognition Systems10.1109/ACCESS.2021.3068045 ↗
3. Emotional voice conversion: Theory, databases and ESD10.1016/j.specom.2021.11.006 ↗
4. A circumplex model of affect10.1037/h0077714 ↗
5. An argument for basic emotions10.1080/02699939208411068 ↗
6. The Geneva Minimalistic Acoustic Parameter Set (GeMAPS) for Voice Research and Affective Computing10.1109/TAFFC.2015.2457417 ↗
7. Vocal communication of emotion: A review of research paradigms10.1016/S0167-6393(02)00084-5 ↗
8. Beyond Correlation: Acoustic Transformation Methods for the Experimental Study of Emotional Voice and Speech10.1177/1754073920934544 ↗
9. Taylor, P. (2009). Text-to-Speech Synthesis, Cambridge University Press.10.1017/CBO9780511816338 ↗
10. The contribution of phonation type to the perception of vocal emotions in German: An articulatory synthesis study10.1121/1.4906836 ↗
11. Birkholz, P. (2013). Modeling Consonant-Vowel Coarticulation for Articulatory Speech Synthesis. PLoS ONE, 8.10.1371/journal.pone.0060603 ↗
12. Burkhardt, F. (2009, January 6–10). Rule-Based Voice Quality Variation with Formant Synthesis. Proceedings of the InterSpeech 2009, Brighton, UK.10.21437/Interspeech.2009-499 ↗
13. A four-parameter model of glottal flow
14. Cross-language differences in how voice quality and f0 contours map to affect10.1121/1.5066448 ↗
15. Murphy, A., Yanushevskaya, I., Ní Chasaide, A., and Gobl, C. (2017, January 20–24). Rd as a Control Parameter to Explore Affective Correlates of the Tense-Lax Continuum. Proceedings of the InterSpeech 2017, Stockholm, Sweden.10.21437/Interspeech.2017-1448 ↗
16. The LF-model revisited. Transformations and frequency domain analysis
17. Contributions of the glottal source and vocal tract cues to emotional vowel perception in the valence-arousal space10.1121/1.5051323 ↗
18. Glottal source processing: From analysis to applications10.1016/j.csl.2014.03.003 ↗
19. Perrotin, O., and McLoughlin, I. (2019, January 15–19). GFM-Voc: A Real-Time Voice Quality Modification System. Proceedings of the InterSpeech 2019, Graz, Austria.
20. Lorenzo-Trueba, J., Barra-Chicote, R., Raitio, T., Obin, N., Alku, P., Yamagishi, J., and Montero, J.M. (2012, January 9–13). Towards Glottal Source Controllability in Expressive Speech Synthesis. Proceedings of the InterSpeech 2012, Portland, OR, USA.10.21437/Interspeech.2012-445 ↗
21. A Comparison between STRAIGHT, Glottal, and Sinusoidal Vocoding in Statistical Parametric Speech Synthesis10.1109/TASLP.2018.2835720 ↗
22. Vocal Effort based Speaking Style Conversion using Vocoder Features and Parallel Learning10.1109/ACCESS.2019.2895923 ↗
23. Vocal tract area functions from magnetic resonance imaging10.1121/1.415960 ↗
24. Phrase-level speech simulation with an airway modulation model of speech production10.1016/j.csl.2012.10.005 ↗
25. Construction and evaluation of a parametric one-dimensional vocal tract model10.1109/TASLP.2018.2825601 ↗
26. Effects of higher order propagation modes in vocal tract like geometries10.1121/1.4906166 ↗
27. FE Modeling of Human Vocal Tract Acoustics. Part I: Production of Czech vowels10.3813/AAA.918051 ↗
28. Acoustic interaction between the right and left piriform fossae in generating spectral dips10.1121/1.4818744 ↗
29. Influence of vocal tract geometry simplifications on the numerical simulation of vowel sounds10.1121/1.4962488 ↗
30. Freixes, M., Arnela, M., Socoró, J.C., Alías, F., and Guasch, O. (2019). Glottal Source Contribution to Higher Order Modes in the Finite Element Synthesis of Vowels. Appl. Sci., 9.10.3390/app9214535 ↗
31. MRI-based vocal tract representations for the three-dimensional finite element synthesis of diphthongs10.1109/TASLP.2019.2942439 ↗
32. Arnela, M., and Guasch, O. (2019, January 9–13). Finite element simulation of /asa/ in a three-dimensional vocal tract using a simplified aeroacoustic source model. Proceedings of the 23rd International Congress on Acoustics (ICA), Aachen, Germany.
33. Finite element generation of sibilants /s/ and /z/ using random distributions of Kirchhoff vortices10.1002/cnm.3302 ↗
34. Schoder, S., Maurerlehner, P., Wurzinger, A., Hauser, A., Falk, S., Kniesburges, S., Döllinger, M., and Kaltenbacher, M. (2021). Aeroacoustic sound source characterization of the human voice production-perturbed convective wave equation. Appl. Sci., 11.10.3390/app11062614 ↗
35. Resonance tuning in vocal tract acoustics from modal perturbation analysis instead of nonlinear radiation pressure10.1016/j.jsv.2020.115826 ↗
36. Arnela, M., Guasch, O., and Freixes, M. (2021, January 11–16). Finite element generation of sung vowels tuning 3D MRI-based vocal tracts. Proceedings of the 27th International Congress on Sound and Vibration (ICSV27), Graz, Austria.
37. Freixes, M., Arnela, M., Alías, F., and Socoró, J.C. (2019, January 20–22). GlottDNN-based spectral tilt analysis of tense voice emotional styles for the expressive 3D numerical synthesis of vowel [a]. Proceedings of the 10th ISCA Speech Synthesis Workshop (SSW), Vienna, Austria.10.21437/SSW.2019-24 ↗
38. Guasch, O., Alías, F., Arnela, M., Socoró, J.C., Freixes, M., and Pont, A. (2021, January 24–25). GENIOVOX Project: Computational generation of expressive voice. Proceedings of the IberSPEECH2021, Valladolid, Spain.10.21437/IberSPEECH.2021-33 ↗
39. Pitch-synchronous waveform processing techniques for text-to-speech synthesis using diphones10.1016/0167-6393(90)90021-Z ↗
40. Automatic refinement of an expressive speech corpus assembling subjective perception and automatic classification10.1016/j.specom.2008.12.001 ↗
41. Towards high-quality next-generation text-to-speech synthesis: A multidomain approach by automatic domain classification10.1109/TASL.2008.925145 ↗
42. Rabiner, L., and Biing-Hwang, J. (1993). Fundamentals of Speech Recognition, Prentice Hall.
43. Reducing audible spectral discontinuities10.1109/89.890070 ↗
44. ITU-R (2003). ITU-R BS.1534-1: Method for the Subjective Assessment of Intermediate Quality Level of Coding Systems, International Telecommunication Union.
45. Jillings, N., De Man, B., Moffat, D., and Reiss, J.D. (August, January 26). Web audio evaluation tool: A browser-based listening test environment. Proceedings of the 12th International Conference in Sound and Music Computing (SMC 2015), Maynooth, Ireland.
46. Individual Comparisons by Ranking Methods10.2307/3001968 ↗
47. Perrotin, O., and McLoughlin, I. (2019, January 12–17). A Spectral Glottal Flow Model for Source-filter Separation of Speech. Proceedings of the 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK.10.1109/ICASSP.2019.8682625 ↗
48. Arnela, M., and Guasch, O. (2020, January 18–20). Tuning MRI-based vocal tracts to modify formants in the three-dimensional finite element production of vowels. Proceedings of the 12th International Conference on Voice Physiology and Biomechanics, Grenoble, France.