{"658987":{"#nid":"658987","#data":{"type":"event","title":"Ph.D. Dissertation Defense - Chuyao Feng","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003ETitle\u003C\/strong\u003E\u003Cem\u003E:\u0026nbsp; \u003C\/em\u003E\u003Cem\u003EIntra-speaker Voice Quality Recognition for Voice Therapy\u003C\/em\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003ECommittee:\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. David Anderson, ECE, Chair, Advisor\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Elliot Moore, ECE\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Christopher Rozell, ECE\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Omer Inan, ECE\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Eva van Leer, GSU\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EAbstract:\u0026nbsp;\u003C\/strong\u003EA critical problem in voice therapy is poor extra-clinical adherence, stemming mainly from patients\u0026rsquo; difficulty replicating and implementing their prescribed voice technique out- side of the therapy session. While clinicians can judge whether a patient\u0026rsquo;s voice quality resembles the individualized therapeutic target or not, patients have difficulty making this judgment themselves. The goal of therapy\u0026mdash;replacing habitual voice production mechanics with optimal ones\u0026mdash;cannot be achieved when patients cannot independently replicate the target voice technique and consistently differentiate it from their habitual voice production while speaking. Tools to help patients are lacking, demonstrating a substantial knowledge gap in clinical voice science. Machine learning methods have the potential to learn an in- dividual\u0026rsquo;s habitual and target voice qualities and subsequently classify future recordings accordingly. Classification results could serve as patient feedback in the clinician\u0026rsquo;s ab- sence. However, machine learning methods have primarily been applied to differentiate voice disorders or distinguish individual speakers from each other rather than identify in- dividual voice quality variations within a speaker. Therefore, this thesis aims to develop a tool that differentiates patients\u0026rsquo; habitual voice quality from their target voice, thereby automating and extending the clinician\u0026rsquo;s judgment to the extra-clinical setting.\u003C\/p\u003E\r\n","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":"","field_summary_sentence":[{"value":"Intra-speaker Voice Quality Recognition for Voice Therapy "}],"uid":"28475","created_gmt":"2022-06-21 12:16:47","changed_gmt":"2022-06-21 12:16:47","author":"Daniela Staiculescu","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2022-06-24T09:00:00-04:00","event_time_end":"2022-06-24T11:00:00-04:00","event_time_end_last":"2022-06-24T11:00:00-04:00","gmt_time_start":"2022-06-24 13:00:00","gmt_time_end":"2022-06-24 15:00:00","gmt_time_end_last":"2022-06-24 15:00:00","rrule":null,"timezone":"America\/New_York"},"extras":[],"groups":[{"id":"434381","name":"ECE Ph.D. Dissertation Defenses"}],"categories":[],"keywords":[{"id":"100811","name":"Phd Defense"},{"id":"1808","name":"graduate students"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1788","name":"Other\/Miscellaneous"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}