Portrait de Mirco Ravanelli

Mirco Ravanelli

Membre académique associé
Professeur adjoint, Concordia University, École de génie et d'informatique Gina-Cody
Professeur associé, Université de Montréal, Département d'informatique et de recherche opérationnelle
Sujets de recherche
Apprentissage profond
Site web
Google Scholar
LinkedIn
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Biographie

Mirco Ravanelli est professeur adjoint à l'Université Concordia, professeur associé à l'Université de Montréal et membre associé de Mila – Institut québécois d’intelligence artificielle. Lauréat du prix Amazon Research 2022, il est expert en apprentissage profond et en IA conversationnelle, et a publié plus de 60 articles dans ces domaines. Il se concentre principalement sur les nouveaux algorithmes d'apprentissage profond, y compris l'apprentissage autosupervisé, continu, multimodal, coopératif et économe en énergie. Mirco Ravanelli a effectué son postdoctorat à Mila, sous la direction du professeur Yoshua Bengio. Il est notamment le fondateur et le chef de file de SpeechBrain, l'une des boîtes à outils en code source ouvert les plus largement adoptées dans le domaine du traitement de la parole et de l'IA conversationnelle.

Étudiants actuels

Hiba Akhaddar
Maîtrise recherche - Concordia
Allan Allan
Baccalauréat - Concordia
Github
Dehestani Amirali
Stagiaire de recherche - Concordia University
Github
Seina Assadian
Collaborateur·rice de recherche - Concordia University
Github
Cordelle Briac
Collaborateur·rice de recherche - Concordia University
Github
Leo Brodeur
Stagiaire de recherche - Concordia
leobrod44@gmail.com
Github
Gallegati Caterina
Stagiaire de recherche - Concordia
Github
Victor Cruz
Maîtrise recherche - Concordia
Luca Della Libera
Doctorat - Concordia
Co-superviseur⋅e :
Cem Subakan
Github
Wagner Drew
Maîtrise recherche - Concordia
Co-superviseur⋅e :
Irina Rish
Github
Gianfranco Dumoulin Bertucci
Maîtrise recherche - Concordia
Site web
Github
Nadine El-Mufti
Maîtrise recherche - Concordia
nadine.el-mufti@mila.quebec
Site web
Github
Google Scholar
Maab Elrashid Ahmed Mohamed
Doctorat - Concordia
Co-superviseur⋅e :
Cem Subakan
Github
Bonzi Francesco
Doctorat - Concordia
Github
Salman Sami Hussain Ali
Collaborateur·rice de recherche - Concordia University
Github
Haoyu Li
Stagiaire de recherche - Concordia Univesity
Github
Eleonora Mancini
Collaborateur·rice alumni - UdeM
Superviseur⋅e principal⋅e :
Cem Subakan
Github
Google Scholar
Jama Mohamud
Doctorat - UdeM
Co-superviseur⋅e :
Yoshua Bengio
Site web
Github
Google Scholar
Pooneh Mousavi
Doctorat - Concordia
Site web
Github
Google Scholar
Fırat Öncel
Doctorat - Concordia
Co-superviseur⋅e :
Laurent Charlin
Site web
Github
Google Scholar
Peter Peter
Postdoctorat - McGill
Site web
Github
Google Scholar
Artem Ploujnikov
Doctorat - UdeM
Francesco Verdini
Stagiaire de recherche - Sapienza University of Rome

Publications

Interpretable Convolutional Filters with SincNet
Mirco Ravanelli
Yoshua Bengio
Deep learning is currently playing a crucial role toward higher levels of artificial intelligence. This paradigm allows neural networks to l… (voir plus)earn complex and abstract representations, that are progressively obtained by combining simpler ones. Nevertheless, the internal "black-box" representations automatically discovered by current neural architectures often suffer from a lack of interpretability, making of primary interest the study of explainable machine learning techniques. This paper summarizes our recent efforts to develop a more interpretable neural model for directly processing speech from the raw waveform. In particular, we propose SincNet, a novel Convolutional Neural Network (CNN) that encourages the first layer to discover more meaningful filters by exploiting parametrized sinc functions. In contrast to standard CNNs, which learn all the elements of each filter, only low and high cutoff frequencies of band-pass filters are directly learned from data. This inductive bias offers a very compact way to derive a customized filter-bank front-end, that only depends on some parameters with a clear physical meaning. Our experiments, conducted on both speaker and speech recognition, show that the proposed architecture converges faster, performs better, and is more interpretable than standard CNNs.
2018-11-23
ArXiv (prépublication)
arxiv.org
arxiv.org
Twin Regularization for online speech recognition
Mirco Ravanelli
Dmitriy Serdyuk
Yoshua Bengio
Online speech recognition is crucial for developing natural human-machine interfaces. This modality, however, is significantly more challeng… (voir plus)ing than off-line ASR, since real-time/low-latency constraints inevitably hinder the use of future information, that is known to be very helpful to perform robust predictions. A popular solution to mitigate this issue consists of feeding neural acoustic models with context windows that gather some future frames. This introduces a latency which depends on the number of employed look-ahead features. This paper explores a different approach, based on estimating the future rather than waiting for it. Our technique encourages the hidden representations of a unidirectional recurrent network to embed some useful information about the future. Inspired by a recently proposed technique called Twin Networks, we add a regularization term that forces forward hidden states to be as close as possible to cotemporal backward ones, computed by a "twin" neural network running backwards in time. The experiments, conducted on a number of datasets, recurrent architectures, input features, and acoustic conditions, have shown the effectiveness of this approach. One important advantage is that our method does not introduce any additional computation at test time if compared to standard unidirectional recurrent networks.
2018-09-02
Interspeech 2018 (publié)
doi.org
arxiv.org
Light Gated Recurrent Units for Speech Recognition
Mirco Ravanelli
Philemon Brakel
Maurizio Omologo
Yoshua Bengio
A field that has directly benefited from the recent advances in deep learning is automatic speech recognition (ASR). Despite the great achie… (voir plus)vements of the past decades, however, a natural and robust human–machine speech interaction still appears to be out of reach, especially in challenging environments characterized by significant noise and reverberation. To improve robustness, modern speech recognizers often employ acoustic models based on recurrent neural networks (RNNs) that are naturally able to exploit large time contexts and long-term speech modulations. It is thus of great interest to continue the study of proper techniques for improving the effectiveness of RNNs in processing speech signals. In this paper, we revise one of the most popular RNN models, namely, gated recurrent units (GRUs), and propose a simplified architecture that turned out to be very effective for ASR. The contribution of this work is twofold: First, we analyze the role played by the reset gate, showing that a significant redundancy with the update gate occurs. As a result, we propose to remove the former from the GRU design, leading to a more efficient and compact single-gate model. Second, we propose to replace hyperbolic tangent with rectified linear unit activations. This variation couples well with batch normalization and could help the model learn long-term dependencies without numerical issues. Results show that the proposed architecture, called light GRU, not only reduces the per-epoch training time by more than 30% over a standard GRU, but also consistently improves the recognition accuracy across different tasks, input features, noisy conditions, as well as across different ASR paradigms, ranging from standard DNN-HMM speech recognizers to end-to-end connectionist temporal classification models.
2018-04-01
IEEE Transactions on Emerging Topics in Computational Intelligence (publié)
doi.org
arxiv.org