Measurement of feedback in voice control and application in predicting and reducing stuttering using machine learning

The PhD investigates: (1) how the brain encodes feedback from the speaker's own speech; (2) the biomarkers that predict moments of pathological stuttering; and (3) a neurophysiological monitoring system for identifying these features to trigger neuro-stimulation as part of a clinical/assistive technology. The work is at the intersection between engineering and psychological research. The PhD will use digital signal processing (DSP) and machine learning (ML), to explore applications of assistive and healthcare technologies to motor-speech disorders.
I work with neuro-stimulation and electroencephalography (EEG) equipment. I use DSP to extract spectral features of EEG signals which indicate the neural oscillations of the underlying network. As part of my BSc in Psychology, I used transcranial alternating current stimulation. This entrains neural networks to oscillate a specific rate. The aims was to yield a frequency specific modulation to speech. My past work has furthered my understanding in image and signal processing and experimental neuroscience.
The PhD is specifically interested with how perturbations to the feedback system alter the brain's response. Combined EEG+fNIRS provides rich data about brain activity. Through EEG+fNIRS during speech studies, the PhD aims to gain insight into the neural mechanisms during alterations to recurrent speech feedback. Using novel ML techniques allows the extraction of biomarkers of fluency to be determined. Support vector machines (SVM) and neural networks (NN) will be used to extract features that predict disfluency. These learning methods are suitable as they are resistant to outliers relative to logistic regression. Using Least absolute shrinkage and election operator (LASSO) with SVMs and multi-class NN's (such as a deep convolutional NN) would allow the evolution of features across dimensions (i.e. Electro-physiological and hemodynamic). Also, these biomarkers can guide design of assistive and clinical technologies. It is unlikely that the cortical and hemodynamic responses of individuals would create a valid set of super-features. Instead, the ML techniques allows idiosyncratic features (aka biomarkers) appropriate for fluency states for individuals. The incorporation of engineering techniques will not only allow a further understanding of the underlying biology but also how to address associated pathological states.
Speech requires the integration of different systems including memory, motor co-ordination and linguistic planning, amongst other factors. Therefore, The current PhD work will require of image and signal processing, artificial intelligence, computational neuroscience, technology and their integration across disciplines. Namely the integration of DSP and AI to trigger assistive technology and/or non-invasive brain stimulation with the intention of reconfiguring the associated neural networks to restore fluency. The work has implications about how feedback systems are used in fluent speech production; combines machine learning, psychology, human computer interaction and neuroscience and provides background on applications of AI to clinical approaches to neurological disorders.