Estimation of Fundamental Frequency from Surface Electromyographic Data
Keigo Nakamura, Matthias Janke, Michael D. Wand, Tanja Schultz · International Conference on Acoustics, Speech, and Signal Processing · 2011
In this paper, we present our recent studies of $F_0$ estimation from the surface electromyographic (EMG) data using a Gaussian mixture model (GMM)-based voice conversion (VC) technique, referred to as EMG-to-$F_0$. In our approach, a support vector machine recognizes individual frames as unvoiced and voiced (U/V), and voiced $F_0$ contours are discriminated by the trained GMM based on the manner of minimum mean-square error. EMG-to-$F_0$ is experimentally evaluated using three data sets of different speakers. Each data set includes almost 500 utterances. Objective experiments demonstrate that we achieve a correlation coefficient of up to 0.49 between estimated and target $F_0$ contours with more than 84% U/V decision accuracy, although the results have large variations.