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
Seina Assadian
Collaborateur·rice de recherche - Concordia University
Github
Cordelle Briac
Collaborateur·rice de recherche - Concordia University
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
Alessio Giuseppe Alessio
Collaborateur·rice de recherche - International School for Advanced Studies (Trieste, Italy)
Salman Sami Hussain Ali
Collaborateur·rice de recherche - Concordia University
Github
Tristan Lueger
Collaborateur·rice de recherche - Concordia University
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
Ritika Dhamija Ritika
Collaborateur·rice de recherche - Concordia University
Github

Publications

The Pytorch-kaldi Speech Recognition Toolkit
Mirco Ravanelli
Titouan Parcollet
Yoshua Bengio
The availability of open-source software is playing a remarkable role in the popularization of speech recognition and deep learning. Kaldi, … (voir plus)for instance, is nowadays an established framework used to develop state-of-the-art speech recognizers. PyTorch is used to build neural networks with the Python language and has recently spawn tremendous interest within the machine learning community thanks to its simplicity and flexibility.The PyTorch-Kaldi project aims to bridge the gap between these popular toolkits, trying to inherit the efficiency of Kaldi and the flexibility of PyTorch. PyTorch-Kaldi is not only a simple interface between these software, but it embeds several useful features for developing modern speech recognizers. For instance, the code is specifically designed to naturally plug-in user-defined acoustic models. As an alternative, users can exploit several pre-implemented neural networks that can be customized using intuitive configuration files. PyTorch-Kaldi supports multiple feature and label streams as well as combinations of neural networks, enabling the use of complex neural architectures. The toolkit is publicly-released along with a rich documentation and is designed to properly work locally or on HPC clusters.Experiments, that are conducted on several datasets and tasks, show that PyTorch-Kaldi can effectively be used to develop modern state-of-the-art speech recognizers.
2019-05-12
ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (publié)
doi.org
arxiv.org
Quaternion Recurrent Neural Networks
Titouan Parcollet
Mirco Ravanelli
Mohamed Morchid
Georges Linarès
Chiheb Trabelsi
Renato De Mori
Yoshua Bengio
Recurrent neural networks (RNNs) are powerful architectures to model sequential data, due to their capability to learn short and long-term d… (voir plus)ependencies between the basic elements of a sequence. Nonetheless, popular tasks such as speech or images recognition, involve multi-dimensional input features that are characterized by strong internal dependencies between the dimensions of the input vector. We propose a novel quaternion recurrent neural network (QRNN), alongside with a quaternion long-short term memory neural network (QLSTM), that take into account both the external relations and these internal structural dependencies with the quaternion algebra. Similarly to capsules, quaternions allow the QRNN to code internal dependencies by composing and processing multidimensional features as single entities, while the recurrent operation reveals correlations between the elements composing the sequence. We show that both QRNN and QLSTM achieve better performances than RNN and LSTM in a realistic application of automatic speech recognition. Finally, we show that QRNN and QLSTM reduce by a maximum factor of 3.3x the number of free parameters needed, compared to real-valued RNNs and LSTMs to reach better results, leading to a more compact representation of the relevant information.
2019-01-01
ICLR.cc/2019/Conference (poster)
openreview.net
openreview.net
Speaker Recognition from Raw Waveform with SincNet
Mirco Ravanelli
Yoshua Bengio
Deep learning is progressively gaining popularity as a viable alternative to i-vectors for speaker recognition. Promising results have been … (voir plus)recently obtained with Convolutional Neural Networks (CNNs) when fed by raw speech samples directly. Rather than employing standard hand-crafted features, the latter CNNs learn low-level speech representations from waveforms, potentially allowing the network to better capture important narrow-band speaker characteristics such as pitch and formants. Proper design of the neural network is crucial to achieve this goal.This paper proposes a novel CNN architecture, called SincNet, that encourages the first convolutional layer to discover more meaningful filters. SincNet is based on parametrized sinc functions, which implement band-pass filters. In contrast to standard CNNs, that learn all elements of each filter, only low and high cutoff frequencies are directly learned from data with the proposed method. This offers a very compact and efficient way to derive a customized filter bank specifically tuned for the desired application.Our experiments, conducted on both speaker identification and speaker verification tasks, show that the proposed architecture converges faster and performs better than a standard CNN on raw waveforms.
2018-12-18
2018 IEEE Spoken Language Technology Workshop (SLT) (publié)
doi.org
arxiv.org
Speech and Speaker Recognition from Raw Waveform with SincNet
Mirco Ravanelli
Yoshua Bengio
Deep neural networks can learn complex and abstract representations, that are progressively obtained by combining simpler ones. A recent tre… (voir plus)nd in speech and speaker recognition consists in discovering these representations starting from raw audio samples directly. Differently from standard hand-crafted features such as MFCCs or FBANK, the raw waveform can potentially help neural networks discover better and more customized representations. The high-dimensional raw inputs, however, can make training significantly more challenging. This paper summarizes our recent efforts to develop a neural architecture that efficiently processes speech from audio waveforms. In particular, we propose SincNet, a novel Convolutional Neural Network (CNN) that encourages the first layer to discover 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 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 computationally efficient than standard CNNs.
2018-12-13
ArXiv (prépublication)
arxiv.org
arxiv.org
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