Software package for the study of speech signals

Authors and copyright holders:

• Repyuk N. S. , Tomsk State University of Control Systems and Radioelectronics (Tomsk, Russia)
• Konev A. A. , Tomsk State University of Control Systems and Radioelectronics (Tomsk, Russia)

• 1. Analiz rynka rechevyh tehnologij i raspoznavanija rechi v mire [Analysis of speech recognition technologies in the world]. Available at: https://www.fortunebusinessinsights.com/industry-reports/speech-and-voice-recognition-market-101382 (Accessed: November 10, 2024).
• 2. Bhatt S., Jain A., Dev A. Continuous Speech Recognition Technologies – A Review. Recent Developments in Acoustics, 2021, pp. 85–94.
• 3. Kataev M.Ju. [Methods of speech command recognition in school information systems]. Speech Technology, 2024, No. 1, pp. 18–34 (in Russ.).
• 4. Truslow E., Hanson H.M. KlattWare tools for acoustic analysis of speech signals. The Journal of the Acoustical Society of America, 2010, vol. 128, No. 4, pp. 2290.
• 5. Martin P. WinPitchPro. A Tool for Text to Speech Alignment and Prosodic Analysis. Speech Prosody, 2004, pp. 1–4.
• 6. Haubold A., Kender J.R. Alignment of speech to highly imperfect text transcriptions. 2007 IEEE International Conference on Multimedia and Expo, 2007, pp. 1–4. DOI: 10.1109/ICME.2007.4284627.
• 7. Mohammed A.A., Nagy A. Fundamental Frequency and Jitter Percent in MDVP and PRAAT. Journal of Voice, 2023, vol. 37, No. 4, pp. 496–503.
• 8. Oğuz H., Kiliç M.A., Şafak M.A. Comparison of results in two acoustic analysis pro-grams: Praat and MDVP. Turkish Journal of Medical Sciences, 2011, vol. 41, No. 5, pp. 835–841.
• 9. Wei M., Du J., Wang X., Lu H., Wang W., Lin P. Voice disorders in severe obstructive sleep apnea patients and comparison of two acoustic analysis software pro-grams: MDVP and Praat. Sleep Breath, 2021, vol. 25, No. 1, pp. 433–439.
• 10. Maryn Y., Corthals P., De Bodt M., Van Cauwenberge P., Deliyski D. Perturbation measures of voice: a comparative study between Multi-Dimensional Voice Program and Praat. Folia Phoniatrica et Logopaedica, 2009, vol. 61, No. 4, pp. 217–226.
• 11. Ofer A. A clinical comparison between two acoustic analysis softwares: MDVP and Praat. Biomedical Signal Processing and Control, 2009, vol. 4, No. 3, pp. 202–205.
• 12. Nicastri M., Chiarella G., Gallo L.V., Catalano M., Cassandro E. Multidimensional Voice Program (MDVP) and am-plitude variation parameters in euphonic adult subjects. Normative study. ACTA Otorhinolaryngologica Italica, 2004, vol. 24, pp. 337–341.
• 13. Keung L.-C., Richardson K., Matheron D.S., MartelSauvageau V. A Comparison of Healthy and Disordered Voices Using Multi-Dimensional Voice Program, Praat, and TF32. Journal of Voice, 2024, vol. 38, No. 4, pp. 23–38.
• 14. Muradov M.T., Nazarova S.N. [Modern speech recognition technologies]. Science and Worldview, 2024, No. 1(23), pp. 301–305 (in Russ.).
• 15. Jakimuk A.Ju., Konev A.A., Osipov A.O. [Program complex for speech signal and vocal performance segmentation modeling automation]. Proceedings of Irkutsk State Technical University, 2017, vol. 21, No. 10(129), pp. 53–64 (in Russ.).
• 16. Konev A.A. Model i algoritmy analiza i segmentacii rechevogo signala. [Speech signal analysis and segmentation model and algorithms], Dissertation for the Candidate of Science degree, Tomsk, 2007. 20 p. (in Russ.).
• 17. Konev A.A., Meshherjakov R.V., Kostjuchenko E.Yu. [Segmentation of speech signals into vocalized and non-vocalized areas based on simultaneous masking]. Avtometriya, 2018, vol. 54, no 4, pp. 51–57 (in Russ.).