The EMG-UKA corpus for electromyographic speech processing

Abstract

This article gives an overview of the EMG-UKA corpus, a corpus of electromyographic (EMG) recordings of articulatory activity enabling speech processing (in particular speech recognition and synthesis) based on EMG signals, with the purpose of building Silent Speech interfaces. Data is available in multiple speaking modes, namely audibly spoken, whispered, and silently articulated speech. Besides the EMG data, synchronous acoustic data was additionally recorded to serve as a reference. The corpus comprises 63 recorded sessions from 8 speakers, the total amount of data is 7:32 hours. A trial subset, consisting of 1:52 hours of data, is freely available for download.