Research in AI applied to Speech and Language

Noé Tits is currently working at CluePoints on clinical data analysis, more specifically on developping Machine Learning models and algorithms to detect clinical data inconsistencies.

Noé Tits obtained his Master of Electrical Engineering specialized in Signals, Systems and Bio-engineering (Magna Cum Laude every year) in June 2017. His Master’s thesis was done in the context of an Erasmus Plus scholarship at the University of the Basque Country in Bilbao (Spain) in the Aholab laboratory specialized in speech processing. He developed a tool for analyzing pathological voices.

His experience also counts research projects in the field of electrical engineering such as simulations of heating of cables and electromagnetic fields in cable glands (Laborelec, GDF Suez), motion analysis (eNTERFACE workshop, Numediart Institute of UMONS), singing voice analysis (Hovertone) and Medical Image Processing (in collaboration with UCB).

In december 2017, Noé obtained a grant from the FNRS to pursue a PhD at the Numediart Institute of UMONS, ISIA Lab. His PhD thesis was focused on the application of Deep Learning techniques for controlling the emotional expressiveness in Text-to-Speech Synthesis.

He took care of the Research and Development of Flowchase’s speech technology to automatically analyze and provide feedback to English learners using Machine/Deep Learning and Signal Processing paradigms.

The developed speech technology presents cutting-edge Deep Learning models for phonetic alignment and classification, facilitating multilingual text phonetization and precise speech alignment. Its efficient model training allows for speech-to-text inference, stress intensity analysis, and nuanced intonation assessment, catering to a wide range of industry applications in speech and language processing. Moreover, the robust Speech & Language Tools offer comprehensive solutions for linguistic data extraction, documentation, dataset parsing, and automatic transcription with segmentation.