[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119143-en":3,"doc-seo-119143-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119143,8796095462418,"Noah","https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780",8,"Research & Report","Measurement of feedback in voice control and application in predicting and reducing stuttering using machine learning","Speech fluency depends on how the brain uses feedback information during speech, and adapting feedback to improve fluency remains insufficiently understood. This PhD develops evidence on feedback in the speech-motor system and applies machine learning to detect, predict, and optimize moments of dysfluency. Original experimental work studies altered sensory feedback in fluent and stuttering speakers, supported by mixed-linear modeling, neurostimulation findings, and fNIRS neuroimaging that links fluency gains to cortical regions. Machine learning models trained on audio signals identify stuttering and support improved standards through a systematic review and tested protocols.","Measurement of feedback in voice control and application in predicting and reducing stuttering using machine learning.  \nLiam Barrett  \nDepartment of Experimental Psychology University College London (UCL)  \nThesis submitted for the degree of  \nDoctor of Philosophy  \nDeclaration  \nI, Liam Barrett, confirm that the work presented in this thesis is my own. Where information has been derived from other sources, I confirm that this has been indicated in the thesis.  \nSigned: Liam Barrett, June 2023 .  \nAcknowledgements  \nThis work has been made possible through the guidance, support, and encouragement through many remarkable people. In particular:  \nI would like to thank Professor Peter Howell, Primary Supervisor, for your unwavering support and continued collaboration. He is a truly inspirational academic.  \nAlso, Dr. Jeremy Skipper, Secondary Supervisor, for his guidance in all things academic and beyond.  \nI would like to thank the colleagues I have worked with within and outside of UCL. Particularly I would like to thank Prof. Stephan Paul, Prof. Kevin Tang, and Ms Junchao Hu.  \nFinally, I would like to thank my family and friends who have given immeasurable support throughout this journey.  \nAbstract  \nHow the brain uses feedback information during speech to maintain fluency is a complex and unresolved process. Additionally, how alterations to feedback can be utilized for speech fluency enhancement may not be optimal. Hence, this thesis seeks to a.) further knowledge on feedback in the speech-motor system, b.) utilize machine learning methods to identify and predict moments of dysfluency and, c.) optimize fluency-enhancing methods with the use of machine learning.  \nTo this end, the thesis includes original, experimental work on altered sensory feedback in people who do and do not stutter. Mixed-linear modeling has revealed how brain stimulation can alter learning of new speech-motor mappings, providing a platform for therapeutic uses for dysfluent populations. Such research has also demonstrated fluency enhancing effects of somatosensory alterations in people who stutter. Adjunct neuroimaging evidence using functional near infrared spectroscopy links these fluency enhancements to specific areas of the cortex, furthering the understanding of speech’s neural mechanisms.  \nMachine learning models-including Logistic Regression, Support Vector Machines and Deep Neural Networks-were trained to identify stuttering from the audio signal. The work conducted on this has had major impacts on how stuttering is approached from a machine learning perspective. In particular, a systematic review alongside experimental testing has improved standards in the field.  \nTogether, the PhD has improved our understanding of feedback in voice control in both fluent and dysfluent speakers. Additionally, how speech-motor control is sub-served by the brain is furthered. The PhD has delivered machine learning methods to identify stuttered speech in conjunction with protocols to enhance fluency. From this work, future  \nresearch will be able to improve stuttering recognition as well as integrate machine learning and fluency enhancement methods to optimize stuttering prostheses.  \nImpact Statement  \nAltered feedback (AFB) enhances fluency in people who stutter (PWS)(Howell, 2004b) . Previous implementation of fluency-enhancing prostheses has affected the auditory information stream, leading to drops in usability as well as information loss to the user. Alterations in the speakers vibrotactile environment also enhance fluency in PWS. This thesis has shown that such techniques can be applied through small handheld devices which are not purpose built for AFB. This could lead to a major change in how AFB is provided to PWS, making this method of fluency enhancement more accessible for PWS as well as reducing the information loss experience in more popular forms of AFB prosthetic (Barrett & Howell, 2021) .  \nNeurostimulation is proposed to be another method thr","cbCaiiCu1sRlF8uI","https://ap.wps.com/l/cbCaiiCu1sRlF8uI","pdf",26034385,1,420,"English","en",105,"# Abstract\n## Altered sensory feedback and neural mechanisms\n## Machine learning for automatic recognition of stuttering\n## Impact on prediction and fluency enhancement","[{\"question\":\"What problem does the thesis address regarding speech fluency?\",\"answer\":\"It investigates how feedback information supports fluency during speech and how changes to feedback can be used to enhance fluency more effectively.\"},{\"question\":\"How does the thesis use machine learning in stuttering research?\",\"answer\":\"It trains machine learning models on audio signals to identify stuttering, and combines systematic review work with experimental testing to improve field standards.\"},{\"question\":\"What evidence links fluency improvements to brain activity?\",\"answer\":\"Functional near infrared spectroscopy (fNIRS) is used to associate fluency enhancements from sensory alterations with specific cortical areas.\"}]","Measurement of feedback in voice control and application in predicting and reducing stuttering using machine learning | 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