Please use this identifier to cite or link to this item:
http://hdl.handle.net/20.500.12188/26203
Title: | Macedonian Speech Synthesis for Assistive Technology Applications | Authors: | Bojan Sofronievski Elena Velovska Martin Velichkovski Violeta Argirova Tea Veljkovikj Risto Chavdarov Stefan Janev Kristijan Lazarev Toni Bachvarovski Zoran Ivanovski Dimitar Tashkovski Branislav Gerazov |
Keywords: | eess.AS eess.AS Computer Science - Sound |
Issue Date: | 18-May-2022 | Conference: | In 2022 30th European Signal Processing Conference, EUSIPCO, pp. 1183-1187, IEEE, Belgrade, Serbia, August 2022. | Abstract: | Speech technology is becoming ever more ubiquitous with the advance of speech enabled devices and services. The use of speech synthesis in Augmentative and Alternative Communication tools, has facilitated inclusion of individuals with speech impediments allowing them to communicate with their surroundings using speech. Although there are numerous speech synthesis systems for the most spoken world languages, there is still a limited offer for smaller languages. We propose and compare three models built using parametric and deep learning techniques for Macedonian trained on a newly recorded corpus. We target low-resource edge deployment for Augmentative and Alternative Communication and assistive technologies, such as communication boards and screen readers. The listening test results show that parametric speech synthesis is as performant compared to the more advanced deep learning models. Since it also requires less resources, and offers full speech rate and pitch control, it is the preferred choice for building a Macedonian TTS system for this application scenario. | URI: | http://hdl.handle.net/20.500.12188/26203 |
Appears in Collections: | Faculty of Electrical Engineering and Information Technologies: Conference Papers |
Show full item record
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.