Articulatory Feature Classification using Surface Electromyography

In this paper, we present an approach for articulatory feature classification based on surface electromyographic signals generated by the facial muscles. With parallel recorded audible speech and electromyographic signals, experiments are conducted to show the anticipatory behavior of electromyographic signals with respect to speech signals. On average, we found that the signals to be time delayed by 0.02 to 0.12 second. Furthermore, it is shown that different articulators have different anticipatory behavior. With offset-aligned signals, we improved the average F-score of the articulatory feature classifiers in our baseline system from 0.467 to 0.502.