Mirco Ravanelli

heidari.allan@mila.quebec

Biography

I am an AI researcher, educator, and open-source leader working on the next generation of intelligent machines. I am also an Associate Professor at Concordia University, Adjunct Professor at Université de Montréal, and Associate Member of Mila – Quebec AI Institute. My research focuses on deep learning for Conversational AI, multimodal foundation models, interpretable AI, and efficient machine learning.

My work has resulted in over 100 scientific publications and has been recognized through international awards, including the Amazon Research Award. I had the privilege of completing my postdoctoral training at Mila under the supervision of Yoshua Bengio.

I am the founder and leader of SpeechBrain, one of the world's most widely adopted open-source toolkits for speech and conversational AI, used by researchers and engineers across academia and industry. SpeechBrain has become a global community effort, helping democratize access to cutting-edge AI technologies.

Beyond research, I am passionate about education and mentorship. I believe that advancing AI requires not only scientific breakthroughs but also training the next generation of researchers and engineers to develop AI responsibly and for the benefit of society.

Research interests: Conversational AI • Multimodal LLMs • Audio Tokens • Representation Learning • Interpretable AI • Efficient Deep Learning • Open-Source AI

Current Students

Allan Allan

Undergraduate - Concordia University

gaspard.botte@mila.quebec

Gaspard Botté

Research Intern - Concordia University

briac.cordelle@mila.quebec

Cordelle Briac

Master's Research - Concordia University University

Leo Brodeur

Master's Research - Concordia University

leobrod44@gmail.com

Matthieu Cervera

Collaborating researcher

Principal supervisor :

matthieu.cervera@mila.quebec

Victor Cruz

Master's Research - Concordia University

victor.cruz@mila.quebec

Luca Della Libera

PhD - Concordia University

Co-supervisor :

luca.dellalibera@mila.quebec

Wagner Drew

Master's Research - Concordia University

Co-supervisor :

Irina Rish

drew.wagner@mila.quebec

Gianfranco Dumoulin Bertucci

Master's Research - Concordia University

gianfranco.bertucci@mila.quebec

nadine.el-mufti@mila.quebec

Nadine El-Mufti

Master's Research - Concordia University

Maab Elrashid Ahmed Mohamed

PhD - Concordia University

Co-supervisor :

maab-elrashid.ahmed-mohamed@mila.quebec

francesco.bonzi@mila.quebec

Bonzi Francesco

PhD - Concordia University

salman.hussainali@mila.quebec

Salman Sami Hussain Ali

Master's Research - Concordia University University

sidra.intikhab@mila.quebec

Sidra Intikhab

Research Intern - Concordia University

Lovenya Jain

Collaborating researcher - Université Laval

Principal supervisor :

f20211732@pilani.bits-pilani.ac.in

Jihoon Jeong

PhD - Université Laval

Principal supervisor :

jihoon.jeong@mila.quebec

Haoyu Li

Professional Master's - Concordia University Univesity

lihaoyuwilson@gmail.com

Eleonora Mancini

Collaborating Alumni - Université de Montréal

Principal supervisor :

eleonora.mancini@mila.quebec

hussein-mohamu.jama@mila.quebec

Jama Mohamud

PhD - Université de Montréal

PhD - Concordia University

pooneh.mousavi@mila.quebec

PhD - Concordia University

Co-supervisor :

Laurent Charlin

firat.oncel@mila.quebec

artem.ploujnikov@mila.quebec

Peter Peter

Postdoctorate - McGill University

PhD - Université de Montréal

Raphael Switzer

Research Intern - Concordia University

raphael.switzer@mila.quebec

benjamin.l.van.niekerk@gmail.com

Benjamin Van Niekerk

Postdoctorate - Concordia University

FocalCodec: Giving LLMs Ears and a Voice at Ultra-Low Bitrates

Blog Posts

Visual of FocalCodec,nouvelle méthode pour compresser la parole sans sacrifier la qualité, en vue d'obtenir des LLM multimodaux plus efficaces.

January 23, 2026

Luca Della Libera

Francesco Paissan

Cem Subakan

Mirco Ravanelli

Read the article

June 13, 2024

SpeechBrain 1.0: Making Conversational AI Accessible to Everyone

Mirco Ravanelli

Read the article

April 28, 2021

Introducing SpeechBrain: A General-Purpose PyTorch Speech Processing Toolkit

Mirco Ravanelli

Loren Lugosch

Read the article

Publications

Multi-Task Self-Supervised Learning for Robust Speech Recognition

Jianyuan Zhong

Santiago Pascual

Pawel Swietojanski

Joao Monteiro

Jan Trmal

Despite the growing interest in unsupervised learning, extracting meaningful knowledge from unlabelled audio remains an open challenge. To t… (see more)ake a step in this direction, we recently proposed a problem-agnostic speech encoder (PASE), that combines a convolutional encoder followed by multiple neural networks, called workers, tasked to solve self-supervised problems (i.e., ones that do not require manual annotations as ground truth). PASE was shown to capture relevant speech information, including speaker voice-print and phonemes. This paper proposes PASE+, an improved version of PASE for robust speech recognition in noisy and reverberant environments. To this end, we employ an online speech distortion module, that contaminates the input signals with a variety of random disturbances. We then propose a revised encoder that better learns short- and long-term speech dynamics with an efficient combination of recurrent and convolutional networks. Finally, we refine the set of workers used in self-supervision to encourage better cooperation. Results on TIMIT, DIRHA and CHiME-5 show that PASE+ significantly outperforms both the previous version of PASE as well as common acoustic features. Interestingly, PASE+ learns transferable representations suitable for highly mismatched acoustic conditions.

2020-05-03

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (published)

Learning Speaker Representations with Mutual Information

Learning good representations is of crucial importance in deep learning. Mutual Information (MI) or similar measures of statistical dependen… (see more)ce are promising tools for learning these representations in an unsupervised way. Even though the mutual information between two random variables is hard to measure directly in high dimensional spaces, some recent studies have shown that an implicit optimization of MI can be achieved with an encoder-discriminator architecture similar to that of Generative Adversarial Networks (GANs). In this work, we learn representations that capture speaker identities by maximizing the mutual information between the encoded representations of chunks of speech randomly sampled from the same sentence. The proposed encoder relies on the SincNet architecture and transforms raw speech waveform into a compact feature vector. The discriminator is fed by either positive samples (of the joint distribution of encoded chunks) or negative samples (from the product of the marginals) and is trained to separate them. We report experiments showing that this approach effectively learns useful speaker representations, leading to promising results on speaker identification and verification tasks. Our experiments consider both unsupervised and semi-supervised settings and compare the performance achieved with different objective functions.

2019-09-12

Interspeech 2019 (published)

The PyTorch-Kaldi Speech Recognition Toolkit

Titouan Parcollet

The availability of open-source software is playing a remarkable role in the popularization of speech recognition and deep learning. Kaldi, … (see more)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.

2019-04-16

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (published)

Speaker Recognition from Raw Waveform with SincNet

Deep learning is progressively gaining popularity as a viable alternative to i-vectors for speaker recognition. Promising results have been … (see more)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.

2018-12-17

2018 IEEE Spoken Language Technology Workshop (SLT) (published)