Speech and Text

The Speech and Text team at CRIM offers various services for data processing and interpretation in fields such as real-time closed-captioning, exploitation of large datasets, multimedia indexing, information protection, audio signal analysis and segmentation, voice biometrics and natural language processing. 

Our expertise

Our team's expertise mainly covers the fields of automated speech recognition, speaker recognition, audio signal processing and  natural language processing (NLP). With all this knowledge, combined with an expertise in probabilistic methods and deep learning, the team is strategically equipped to contribute to scientific advancement and to respond to companies' specific needs. The team has developed numerous ground-breaking algorithms that have been adopted worldwide.

See all expertise

Profile of an expert

Gilles Boulianne
Gilles Boulianne
Senior Researcher in Automatic Speech Processing

Since joining CRIM in 1998, Gilles Boulianne has been conducting research in signal processing, speech and speaker recognition, and their applications such as audio document indexing, automated transcription and live captioning.

Contact The Speech and Text Team

For any information, please contact
Hans Bherer, director.

514 840-1235, poste 4624

Recent news

  • Enjeux de l’exploitation des données : une première conférence réseautage dynamique et riche d’échanges!

    La première conférence réseautage organisée par le CRIM et Lavery Avocats fut un franc succès, attirant une quarantaine de personnes dans les bureaux de Lavery.


Upcoming event

  • Sécurité de l'information 2019 - Événement Les Affaires
    20 March 2019 8:15
    Centre-Ville, Montréal
    Le CRIM fier partenaire de la 5e édition de la conférence sur la «Sécurité de l'information», organisée par les Événements Les Affaires, qui se tiendra le 20 mars 2019 au Centre-Ville de Montréal.

Recent Publications

  • Object Counting on Low Quality Images: A Case Study of Near Real-Time Traffic Monitoring

  • Object Counting on Low Quality Images: A Case Study of Near Real-Time Traffic Monitoring