Skip to main content
ENHU
Home

Main navigation

  • Discover
    • News
    • Events
    • Tenders
  • Research fields
  • Resources
    • Publications
    • Downloads
  • About us
  • Partners
  1. Home

Bálint Gyires-Tóth

Researcher
Budapest University of Technology and Economics
Széchenyi Plusz RRF

Bálint Gyires-Tóth is a senior lecturer at BME. He conducts research on fundamental and applied machine learning since 2007. With his leadership, the first Hungarian hidden Markov-model based Text-To-Speech (TTS) system was introduced in 2008. He obtained his PhD degree with summa cum laude in January 2014. Since then, his primary research field is deep learning. His main research interests are sequential data modeling with deep learning, self-supervised learning and deep reinforcement learning. He also participates in applied deep learning projects, like time series classification and forecast, anomaly detection, image and audio classification and natural language processing. He was involved in various successful research and commercial projects. In 2017 he was certified as NVidia Deep Learning Institute (DLI) Instructor and University Ambassador.

  • MTMT

Projects related to AI : 

  • 2005-2012 Industrial application of ambient intelligent system, Hungarian-German cooperation, BelAmi NAP-2005 project, NKFIH
  • 2005-2007 BESZTEL project: Introducing speech synthesis in mobile phones for vision-impaired users (GVOP-3.1.1.-2004-05-0485/3.0), NKFIH
  • 2009-2011 ETOCOM (TÁMOP-4.2.2/08/1/KMR-2008-0007): Synthetic speech enhancement in infocommunication systems
  • 2011-2013 CESAR (CEntral and South EuropeAn Resources, CIP ICT-PSP-2010-4 no. 271022), speech and language resource standardization and enhancement, EU
  • 2012-2014 Personal Assistant to Enhance the Social Life of the Seniors (PAELIFE), Research on speech synthesizers for elderly people (B-AAL-08-1-2011-0063870), AAL EU
  • VUK (AAL-2014-1-183): The goal of the Visionless sUpporting frameworK (VUK) is to ease daily life for blind and visually impaired people in challenging tasks like participating in urban mobility, providing a simple, effective and affordable door to door navigation and mobility assistance solution, AAL EU
  • 2016-2019 EUREKA / DANSPLAT: A platform for the applications of speech technologies on smartphones for the languages of the Danube region
  • EFOP-3.6.2-16-2017-00013: Modeling weakly coherent multi-dimensional time series with deep learning, subproject leader, NKFIH
  • Felsőoktatási Intézményi Kiválósági Program (FIKP): Adaptive systems and intelligent interactions, subproject leader, NKFIH
  • János Bolyai Research Fellowship, 2019-2021, Modeling multiscale sequential data with deep learning, Hungarian Academy of Sciences
  • New National Excellence Program, Bolyai+ Young Higher Education Teachers and Researchers Fellowship, 2019-2021, NKFIH
  • "Professional Intelligence for Automotive" (PIA), Continental Deep Machine Learning Competence Center
  • The European Sector Skills Alliances, 2020-2023, EU
Home

LinkedIn

Become a partner

Subscribe to newsletter

Send partnership request

Explore

  • News
  • Events
  • Tenders
  • Publications
  • Downloads
  • Partners

Research fields

  • Foundations of AI
  • Human Language Processing
  • Machine perception
  • Medical, Health and Biology
  • Security and Privacy
  • Sensors, IoT and Telecommunications

Contact us

Hungary, H-1111 Budapest,
Kende u. 13-17.
+36 1 279 6000
@email

© 2020-2021 Artifical Intelligence National Laboratory, Budapest