I am a PhD student in data science at L’IRIT (Toulouse). I am working on corpora with a limited amount of impaired speech. I also keep a blog about visualizations I make, research I do and tips around Linux. Check it out.
I am focused on machine learning algorithms to model knowledge with few or many data. More specifically I like to use self-supervised learning to extract knowledge from data.
In my thesis I adapted Deep Neural Networks techniques in a few-shots context for speech signals. To have more information about it, look into my first-year review as a PhD student post.
I do data analysis to produce some visualizations (most of them are available in my blog) and I participate to some open source.
People with ENT cancers have speech difficulties after surgery or radiation therapy. It is important for the practitioner to have a measure that reflects the severity of speech. I propose two approaches to create an automatic measure, although with little data (about 1h of audio recordings for 128 speakers). The first one is based on “few shot” methods, while the second one is based on entropic measurement of speech features (learned with a self-supervised model on an annexed corpus). Our results on the latter have allowed us to consider a medical application. Thus, I obtained a grant to supervise an engineer in order to realize an application delivered to the Toulouse University Hospital.