Prosodic Preferences of Surface Electromyography-based Subvocal Speech for People With Laryngectomy

People who undergo a total laryngectomy lose their natural voice and depend on alaryngeal technologies for communication. However, these technologies are often difficult to use and lack prosody. Surface electromyographic-based silent speech interfaces are novel communication systems that overcome many of the shortcomings of traditional alaryngeal speech and have the potential to seamlessly incorporate individualized prosody. The purpose of this study was to (1) validate the ability of alaryngeal silent speech to effectively incorporate pitch modulations-a key prosodic element in natural speech-into synthesized speech assessed through listening experiments and (2) determine the key features of these communication devices according to core users.

People with laryngectomy (n = 15) and their primary communication partners (n = 5) listened to synthesized sentences with differing prosodic content generated from deep regression neural networks developed in our prior work. Specifically, the fundamental frequency (f_o) contour of each sentence was manipulated in four ways: (1) flattened to the average f_o, (2) altered to discrete sentence-level classification of muscle activity, (3) altered to continuous mapping of muscle activity, and (4) filtered to emulate speech from an electrolarynx (EL). Listeners ranked the f_o contours of each sentence in terms of speech naturalness and the importance of various speech aid features.

Continuous contours rated higher than all other types of contours, and monotonic EL contours rated the lowest. Speech aid features were rated highest to lowest in the following order: sound quality, intelligibility, pitch, delay, volume, hands-free, maintenance, cost, wearability, training, and visibility.

These results will help inform future development of silent speech interfaces and shape priorities of communication devices toward the preferences of their users.