Macedonian Speech Synthesis for Assistive Technology Applications

TL;DR

This paper develops and compares parametric and deep learning speech synthesis models for Macedonian, aiming to support assistive communication tools for low-resource language scenarios, and finds parametric methods are resource-efficient with comparable quality.

Contribution

It introduces Macedonian speech synthesis models using both parametric and deep learning techniques, tailored for assistive technology applications in low-resource settings.

Findings

Parametric synthesis performs comparably to deep learning models in listening tests.

Parametric models require fewer resources and allow full control over speech parameters.

The study provides a new Macedonian speech corpus for TTS development.

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.