Macedonian Speech Synthesis for Assistive Technology Applications

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.

Scorpiano -- A System for Automatic Music Transcription for Monophonic Piano Music

Music transcription is the process of transcribing music audio into music notation. It is a field in which the machines still cannot beat human performance. The main motivation for automatic music transcription is to make it possible for anyone playing a musical instrument, to be able to generate the music notes for a piece of music quickly and accurately. It does not matter if the person is a beginner and simply struggles to find the music score by searching, or an expert who heard a live jazz improvisation and would like to reproduce it without losing time doing manual transcription. We propose Scorpiano -- a system that can automatically generate a music score for simple monophonic piano melody tracks using digital signal processing. The system integrates multiple digital audio processing methods: notes onset detection, tempo estimation, beat detection, pitch detection and finally generation of the music score. The system has proven to give good results for simple piano melodies, comparable to commercially available neural network based systems.