Associate Professor — Maître de Conférences
Laboratoire Informatique d'Avignon (LIA)
Avignon Université (FR)
au

Remote Visiting Scholar
Machine Learning Systems Lab
University of Cambridge (UK)
au

From september 2020, I joined the Laboratoire Informatique d'Avignon (LIA) and Avignon Université as an associate professor (Maître de Conférences) in computer science. I am also a member of the Cambridge Machine Learning Systems Lab as a remote visiting scholar to build strong collaborations with Avignon Universite around shared projects. My research focuses on artificial intelligence through the development of new deep learning methods via self-supervised / representation learning. I am also interested on the efficiency problematic of large deep learning models alongside with finding elegant solutions to the privacy concerns raised by the big data. In this extent, I investigate different potential solutions including federated learning and privacy preserving transformation of latent representations. As of now, I have mostly applied these new concepts to automatic speech processing. I am involved in the management of community driven solutions such as SpeechBrain, an open source toolkit for speech processing and deep learning entirely written in PyTorch. Finally, I am particularly attached to the concept of AI for Social Good, and I therefore dedicate a part of my research time to solve concrete societal problems, such as the astonishing carbon footprint of modern deep learning models.

From february to august 2020, I was part of the Oxford Machine Learning Systems (OxMLSys) as a Senior Research Associate working in collaboration with Prof. Nicholas Lane, working on the fundations of the above described topics.

Prior to joining Oxford, I did my PhD thesis at the University of Avignon and the Laboratoire Informatique d'Avignon (LIA) under the supervision of Prof. Linarès Georges and Assoc. Prof. Morchid Mohamed. The thesis was part of an industrial collaboration (CIFRE) with Orkis, a French company specialised in assets and data managements. The thesis ended with more than 15 publications in well-known conferences and journals, and released new tools to the research community to develop and investigate quaternion neural networks in the context of natural language processing and image processing.

During the thesis, I was given the opportunity to spend 4 months working at the Montréal Institute for Learning Algorithms (MILA), Montréal under the supervision of Prof. Yoshua Bengio. The collaboration mainly concerned the development of new quaternion convolutional and recurrent neural networks for automatic speech recognition. This period is also at the origin of long-term and funded projects including Pytorch-Kaldi and SpeechBrain.

Research Interests


  • Deep learning
  • Efficient automatic speech processing technologies
  • Self-supervised & unsupervised & representation learning
  • AI for social good

Efficient Automatic Speech Processing

The term efficiency defines different problematics. My research focuses on three of them, including better performances of speech processing models, a better management of the available resources (i.e. training data), and finally making these models more efficient to be deployed on constrained systems. Overall, I am pursuing the ultimate goal of a light-weight task-agnostic speech processing system that would be trained with a combination of self-supervised, supervised and federated learning to ensure strong performance and data privacy. My recent work includes the investigation of distillation methods for acoustic models, self-supervised learning, new trully end-to-end speech processing methods and federated learning.

Funded Projects


LeBenchmark (2020 - 2021) Leader of the self-supervised models task

LeBenchmark project has been granted 200K hours of GPU time in the French datacenter Jean Zay. This project is a joint collaboration between the Laboratoire d'Informatique de Grenoble (LIG) and the Laboratoire Informatique d'Avignon (LIA) that aims to collect large quantities of raw speech in French (i.e. several thousand hours) with different styles (read speech, prepared speech, spontaneous speech), from various speakers and use them to learn self-supervised models to be shared with the research community. Furthermore, we also wish to establish a new benchmark data set for several speech processing tasks. I am in charge of finding the appropriate self-supervised methods (e.g wav2vec, PASE, MockingJay) and to deploy it on our new dataset. We wish to release various pre-trained models with a training set acounting for up to 10,000 hours of speech in French. Everything is nicely integrated to SpeechBrain for peoples interested in re-using our models for downstream tasks!

Les modèles sont disponibles sur notre HuggingFace , tandis que le benchmark est disponible sur notre GitHub .

SpeechBrain (2019 - ) Co-coordinator

The SpeechBrain project aims to develop an open-source and all-in-one toolkit based on PyTorch. The goal is to develop a single, flexible, and user-friendly toolkit that can be used to easily develop state-of-the-art speech systems for speech recognition (both end-to-end and HMM-DNN), speaker recognition, speech separation, multi-microphone signal processing (e.g, beamforming), self-supervised learning, and many others. The project is funded thanks to generous donations from five sponsors: the Montréal Institute for Learning Algorithms (MILA), Samsung, NVIDIA, Dolby and ViaDialog. SpeechBrain also benefits from the collaboration and expertise of around 20 different partner institutions (both academics and industrials) ranging from the University of Cambridge to HuggingFace. SpeechBrain already beats all the other toolkits on the considered datasets and with a much easier interface to play with. I'm co-leading SpeechBrain with Dr. Ravanelli Mirco, currently a post-doctoral researcher at MILA.

Have a look at: SpeechBrain!

Advised Ph.D. Students and Interns


Salah ZAIEM — Ph.D. Student — Started on October 2020 and is co-advised with Prof. Slim ESSID from Telecom Paris Sud.
Title: Informed Self-Supervised Speech Representations Learning.
Ideas: Self-supervised learning methods for speech are mostly empirically driven. In particular, there exist very few theoretical evidences on why a method performs better than an other one. With this thesis, we aim at providing theoreticaly grounded tools to design SSL models in an informed manner. For instance, we developed a solution to design a PASE-like architecture without the need for pre-text task search with empirical validation, potentially saving weeks of training (and compute / carbon emissions).

Adel Moumen — Undergrad. Intern — June 2021 to August 2021.
Title: On the limitations of LiGRU networks.
Ideas: Adel investigates different ways of making LiGRU a mandatory alternative to LSTM/GRU for speech processing. There are theoretical and empirical evidences that LiGRU simply are better than LSTM and GRU. Unfortunately, they suffer from a poor GPU implementation and an instability in the recurrent connection. These problems will be tackled during the internship.

SpeechBrain Interns — Started on January 2020.
Format: this is a list of all the researchers that I mentored during their internship to work on SpeechBrain. Internships took place either in Avignon (LIA) or Montréal (Mila) and were co-advised with Dr. Ravanelli
Topics: all the topics and concepts developed within SpeechBrain.

Aku Rouhe, Aalto University (FI).
Peter Plantinga, now at JP Morgan.
Loren Lugosch, Mila (CA).
Ju-Chieh Chou, National Taiwan University (TW).
Abdel Heba, Linaroga / University of Toulouse (FR).
Samuele Cornell, now at Amazon.
Jianyuan Zhong, University of Rochester (USA).

Community Contributions


Workshop Co-Organizer

ICML self-Supervision in Audio and Speech (held virtually), July 17th (2020), Vienna (Austria).

Tutorials

Interspeech 2021: "SpeechBrain: Unifying Speech Technologies and Deep Learning With an Open Source Toolkit", August 2021.
The University of Sheffield: "SpeechBrain: Unifying Speech Technologies and Deep Learning With an Open Source Toolkit", June 2021.

Invited Talks

THE 2ND ANNUAL FEDERATED & DISTRIBUTED/ DECENTRALIZED MACHINE LEARNING CONFERENCE (remote): "Can Federated Learning Save the Planet", June 16th (2021).
Flower Summit 2021 (remote): "Federated speech technologies made easy: Flower and SpeechBrain", March 11th (2021).
Centre de Recherche en Automatique de Nancy (FR): "Should we use quaternion neural networks? Recent advances and limitations.", March 29th (2021).
Samsung AI Cambridge (UK): "SpeechBrain", February 27th (2021).
Samsung AI Cambridge (UK): "Quaternion neural networks", July 10th (2019).
University of Oxford (UK): "Quaternion neural networks", July 9th (2019), Computer Science Department.

Program Committee Member

  • IEEE Signal Processing Letters. 2021
  • IEEE Transactions on Neural Networks and Learning Systems. 2019, 2020
  • IEEE International Journal of Wavelets, Multiresolution and Information Processing. 2020
  • ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies. 2020
  • ACM Multimedia. 2021
  • Springer, Neural Processing Letters. 2020
  • IEEE Transactions on Image Processing. 2020
  • NeurIPS. 20(18-21), top 10% best reviewer 2020.
  • ICLR. 20(19-20-21), top 10% best reviewer 2020.
  • INTERSPEECH. 20(19-20-21)
  • ICASSP. 2020

Expert for Research Projects Evaluation

  • Agence Nationale de la Recherche. 2021
  • Idex Lyon St-Étienne. 2021

Publications

International Journals

International conferences

  • SpeechBrain: A General-Purpose Speech Toolkit
  • Mirco Ravanelli · Titouan Parcollet · Peter Plantinga · Aku Rouhe · Samuele Cornell · Loren Lugosch · Cem Subakan · Nauman Dawalatabad · Abdelwahab HEBA · Jianyuan Zhong · Ju-Chieh Chou · Sung-Lin Yeh · Szu-Wei Fu · Elena Rastorgueva · François Grondin · William Aris · Hwidong Na · Yan Gao · Renato De Mori · Yoshua Bengio
  • Submitted to NeurIPS 2021.
  • Can Federated Learning Save The Planet ?
  • Xinchi Qiu · Titouan Parcollet · Nicholas Lane
  • NeurIPS 2020 : Tackling Climate Change with Machine Learning Workshop
  • December 11-12, Virtual Conference (COVID)
  • Quaternion Recurrent Neural Networks
  • Titouan Parcollet · Mirco Ravanelli · Mohamed Morchid · Georges Linarès · Chiheb Trabelsi · Renato De Mori · Yoshua Bengio
  • ICLR 2019
  • May 6-9 2019, New Orleans, (USA)

National conferences

Teaching

All the tutorials and practical work classes are dispensed in addition to my standard research job

Year 2019/2020

TeachingTutorialsPractical WorkTotal
Innovation Application (AI)12h12h
Total12h

Year 2018/2019

TeachingTutorialsPractical WorkTotal
Computer Science Basics10h10h
Object Oriented Programming C++14h14h28h
Tools for Machine Learning12h12h
Total50h

Year 2017/2018

TeachingTutorialsPractical WorkTotal
Advanced programming and projects (B.Sc.)12h19h31h
Total31h

Year 2016/2017

TeachingTutorialsPractical WorkTotal
Advanced programming and projects (B.Sc.)12h18h30h
Agorithms and programming (B.Sc.)12h18h30h
Total60h

Year 2015/2016

TeachingTutorialsPractical WorkTotal
C2I Certification (B.Sc.)144h
Total144h

Year 2013/2014

TeachingTutorialsPractical WorkTotal
C2I Certification (B.Sc.)144h
Total144h

CV

Education

  • PhD in computer science (CIFRE) - 2019
  • Thesis: Quaternion neural networks
  • Advisors: Georges Linarès, Mohamed Morchid
  • Reviewers: Thierry Artières, Allexandre Allauzen
  • Committee: Yoshua Bengio, Benjamin Lecouteux, Xavier Bost, Nathalie Camelin
  • Avignon Université, France & Orkis, Aix-en-provence, France
  • Master Research in computer science - 2016
  • Thesis: Quaternions and deep neural networks
  • Avignon Université, France
  • Bachelor in computer science - 2014
  • Avignon Université, France

Experiences

  • Visiting Scholar (remote) - 2020 / 2022
  • Cambridge Machine Learning Systems Lab
  • University of Cambridge
  • Associate Professor at Avignon Université - 2020
  • Laboratoire Informatique d'Avignon (LIA)
  • 339 chemin des Meinajaries, 84000 Avignon, France
  • Senior Research Associate - 2020
  • Advisors: Nicholas Lane

  • The research focuses on efficient automatic speech recognition with an emphasis on representation learningm new ways of representing artificial neurons and self-supervised learning. I'm also involved in the supervision of students within the group.

  • University of Oxford, Department of Computer Science, OxMLSys lab, Oxford, United-Kingdom
  • CIFRE PhD Student - 2017 / 2019
  • Thesis: Quaternion neural networks
  • Advisor: Georges Linarès, Mohamed Morchid

  • PhD Student in machine learning working on quaternion-valued neural networks with applications to speech recognition, spoken language understanding and image processing. Research engineer at ORKIS, working on new techniques on ASR and documents management with deep learning. Teacher assistant for Master and Bachelor students. Given courses mainly focused on machine learning, programming and algorithms.

  • Avignon Université, France & Orkis, Aix-en-provence, France
  • Montréal Institute for Learning Algorithms (MILA) - Jan. 2018 / Apr. 2018
  • Advisor: Yoshua Bengio

  • Collaboration and release of the Pytorch-Kaldi toolkit. Research on quaternion convolutional neural networks for end-to-end automatic speech recognition. Introduction of quaternion-valued recurrent neural networks to speech recognition.

  • Université de Montréal, Québec, Canada
  • Research Engineer - Sep. 2016 / Mar. 2017
  • Advisor: Mohamed Morchid

  • Hyper-complex neural networks implementation on GPU (CUDA). Research on Spoken Language Understanding (Pattern Recognition).

  • Avignon Université, France

Contact

Mail

titouan.parcollet@univ-avignon.fr

Address

339 Chemin des Meinajaries, 84000 Avignon, France