论文标题
辅助技术应用的马其顿语音综合
Macedonian Speech Synthesis for Assistive Technology Applications
论文作者
论文摘要
随着启用语音的设备和服务的发展，语音技术变得越来越普遍。语音合成在增强和替代交流工具中的使用促进了言语障碍的人，使他们可以使用语音与周围环境进行交流。尽管对于口语最多的世界语言有许多语音综合系统，但对于较小的语言仍然有限。我们建议并比较了使用参数和深度学习技术构建的三个模型，该模型是在新录制的语料库中训练的马其顿人。我们以低资源边缘部署为目标，用于增强和替代通信以及辅助技术，例如通信板和屏幕读者。听力测试结果表明，与更先进的深度学习模型相比，参数语音合成是表现的。由于它也需要更少的资源，并提供全部语音速率和音高控制，因此它是为此应用程序构建马其顿TTS系统的首选选择。
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.
