Abstract
For children with severe cerebral palsy (CP), social and emotional interactions can be significantly limited due to impaired speech motor function. However, if it is possible to extract continuous voluntary control signals from the electromyograph (EMG) of limb muscles, then EMG may be used to drive the synthesis of intelligible speech with controllable speed, intonation and articulation. We report an important first step: the feasibility of controlling a vowel synthesizer using non-speech muscles. A classic formant-based speech synthesizer is adapted to allow the lowest two formants to be controlled by surface EMG from skeletal muscles. EMG signals are filtered using a non-linear Bayesian filtering algorithm that provides the high bandwidth and accuracy required for speech tasks. The frequencies of the first two formants determine points in a 2D plane, and vowels are targets on this plane. We focus on testing the overall feasibility of producing intelligible English vowels with myocontrol using two straightforward EMG-formant mappings. More mappings can be tested in the future to optimize the intelligibility. Vowel generation was tested on 10 healthy adults and 4 patients with dyskinetic CP. Five English vowels were generated by subjects in pseudo-random order, after only 10 min of device familiarization. The fraction of vowels correctly identified by 4 naive listeners exceeded 80% for the vowels generated by healthy adults and 57% for vowels generated by patients with CP. Our goal is a continuous “virtual voice” with personalized intonation and articulation that will restore not only the intellectual content but also the social and emotional content of speech for children and adults with severe movement disorders.
Highlights
Children with brain injury in the perinatal period, usually referred as Cerebral Palsy (CP), are often left with a combination of weakness, spasticity, dystonia, dyspraxia, and other motor disorders (Cans, 2000)
Since the drive signal x is determined by voluntary behavior, we model this behavior as a jump-diffusion process that includes the possibility of gradual changes in muscle drive with occasional sudden jumps at the time of force onset or offset: dx = α + (U − x) dNβ where the stochastic differential equation is to be interpreted in the Ito sense, dW is the differential of a standard Brownian motion, dNβ is the differential of a counting process with rate β events per second, and x is a random variable uniformly distributed on [0,1]
Notice the abrupt jump in the bottom-left part of the trajectory shown in Figure 5B, this is because non-linear Bayesian filtering allows for rapid jumps even though the main purpose is still acquisition of smooth control signals
Summary
Children with brain injury in the perinatal period, usually referred as Cerebral Palsy (CP), are often left with a combination of weakness, spasticity, dystonia, dyspraxia, and other motor disorders (Cans, 2000). In the most severe cases, the motor disorders in CP can prevent all meaningful voluntary movements of the patient (Sanger et al, 2003), and more than 80% of children with dyskinetic or tetraplegic CP suffer from speech impairments (Odding et al, 2006) While new therapies such as stem cells hold great promise for the treatment of early brain injuries, full restoration of speech for children with CP remains unlikely
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