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Mid-Term Review 15 February 2019, Lisbon

Mid-Term Review 15 February 2019, Lisbon. Secondary supervision Non- academic : EML Clinical : Centre Hospitalier Universitaire de Toulouse. Eugenia Rykova ESR 6 , WP 2 Contract start date: 15.09.2018 Host Institute: IRIT Supervisor(s): Dr. Julie Mauclair ,

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Mid-Term Review 15 February 2019, Lisbon

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  1. Mid-Term Review15 February 2019, Lisbon Secondary supervision Non-academic: EML Clinical: Centre Hospitalier Universitaire de Toulouse Eugenia Rykova ESR6, WP2 Contract start date: 15.09.2018 Host Institute:IRIT Supervisor(s): Dr. Julie Mauclair, Prof.Dr. Julien Pinquier PhD student at Université Paul Sabatier This project has received funding from the European Union's Horizon 2020 research and innovation programme under Marie Sklodowska-Curie grant agreement No 766287.

  2. Background • 2006– 2010 BA withdistinction in Philology(Saint Petersburg State University, Russia) • Therealisation of orthoepiclaws for consonants at thelanguageperiphery. • Russian generalquestion intonated by native Turkish speakers. • 2010 – 2016 Speech Technology Center (Biometrics Project in Ecuador), IT Sales, Teaching (Russia, Mexico, Qatar) • 2016 – 2018 MSc in ClinicalLinguistics (University of Eastern Finland, University of Potsdam, University of Groningen) • Perceptual and acoustic similarities between the voices of family members: an approach to synthesize a voice based on family-shared f0 characteristics ESR6, WP2

  3. Role in the Project & Objectives Project: Deep learning approaches to assess head and neck cancer voice intelligibility How does a trained neural network (NN) “react” to the acoustic features preserved in pathological speech under the condition of absence of other features? Can the changes in the NN’s behaviour indicate the type of pathology (e.g., the location of surgery)? Are the same preserved features important for intelligibility for both an NN and a human listener? ESR6, WP2

  4. ResearchMethodology, Results & NextSteps Understanding and visualizing the behaviour (activations and classification output probabilities) of a trained NN in response to certain input (e.g., selected phones or group of phones). Looking at the same NN's behaviour in response to pathological speech, namely audio collected from people who have undergone treatment due to head and neck cancer, and compare the results to the ones obtained on the "clean speech". Listeningexperiments with human subjects. ESR6, WP2

  5. PlannedSecondments European Media Laboratory, Heidelberg (March – Sep) Different NN architectures for speech recognition, seq2seq recognition. Working with existing tools for ASR, possibly applying them to the corpus of pathological speech from IRIT. Language/accent/age adaptation of existing models - to try later adaptation for pathological speech. Particularities of working with children's speech variety. Interaction with Health Care sector. ESR6, WP2

  6. Training, Conferences & Workshops • TAPAS training events and workshops (every 6 months) • Artificial intelligence and machine learning summer school in Trondheim (June) • Eastern European Machine Learning Summer School (July) • Speech Processing Courses in Crete • Interspeech (September) •  Nordic Conference on Computational Linguistics (October) ESR6, WP2

  7. Outreach, Dissemination& Networking Communicating with Health Care sector as a potential client, understanding the needs of the industry and society. Creating a course on Experimental Phonetics (possibility to teach as an Erasmus Mundus alumna) Presenting the research for younger generations (e.g., school children). ESR6, WP2

  8. Impact Personal career plans Working in R&D preferably in commercial sector, combining knowledge in Clinical Linguistics and Computer Science. If staying in academia, possibly shifting to Computational Neuroscience area. ESR6, WP2

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