Mirco Ravanelli

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

Dehestani Amirali

Stagiaire de recherche - Concordia University

Seina Assadian

Collaborateur·rice de recherche - Concordia University

Cordelle Briac

Collaborateur·rice de recherche - Concordia University

Leo Brodeur

Stagiaire de recherche - Concordia

leobrod44@gmail.com

Victor Cruz

Maîtrise recherche - Concordia

Luca Della Libera

Doctorat - Concordia

Co-superviseur⋅e :

Wagner Drew

Maîtrise recherche - Concordia

Co-superviseur⋅e :

Irina Rish

Gianfranco Dumoulin Bertucci

Maîtrise recherche - Concordia

nadine.el-mufti@mila.quebec

Nadine El-Mufti

Maîtrise recherche - Concordia

Maab Elrashid Ahmed Mohamed

Doctorat - Concordia

Co-superviseur⋅e :

Doctorat - Concordia

Salman Sami Hussain Ali

Collaborateur·rice de recherche - Concordia University

Jihoon Jeong

Doctorat - Université Laval

Superviseur⋅e principal⋅e :

Haoyu Li

Stagiaire de recherche - Concordia Univesity

Eleonora Mancini

Collaborateur·rice alumni - UdeM

Superviseur⋅e principal⋅e :

Doctorat - UdeM

Co-superviseur⋅e :

Doctorat - Concordia

Doctorat - Concordia

Co-superviseur⋅e :

Peter Peter

Postdoctorat - McGill

Doctorat - UdeM

Stagiaire de recherche - Sapienza University of Rome

Alameda Pineda Xavier

Visiteur de recherche indépendant - INRIA

SpeechBrain 1.0 : rendre l’IA conversationnelle accessible à tout le monde

Billets de blogue

13 juin 2024

par

Mirco Ravanelli

Lire l'article

Introducing SpeechBrain: A general-purpose PyTorch speech processing toolkit

28 avril 2021

Voici SpeechBrain : Une boîte à outils polyvalente de traitement de la parole basée sur PyTorch

par

Mirco Ravanelli

Loren Lugosch

Lire l'article

Publications

Audio Prototypical Network for Controllable Music Recommendation

Traditional recommendation systems represent user preferences in dense representations obtained through black-box encoder models. While thes… (voir plus)e models often provide strong recommendation performance, they lack interpretability for users, leaving users unable to understand or control the system’s modeling of their preferences. This limitation is especially challenging in music recommendation, where user preferences are highly personal and often evolve based on nuanced qualities like mood, genre, tempo, or instrumentation. In this paper, we propose an audio prototypical network for controllable music recommendation. This network expresses user preferences in terms of prototypes representative of semantically meaningful features pertaining to musical qualities. We show that the model obtains competitive recommendation performance compared to popular baseline models while also providing interpretable and controllable user profiles.

2025-07-31

ArXiv (prépublication)

openreview.net

Autoregressive Speech Enhancement via Acoustic Tokens

Luca Della Libera

2025-07-01

arXiv (publié)

Does Language Matter for Early Detection of Parkinson's Disease from Speech?

Peter William VanHarn Plantinga

Briac Cordelle

Dominique Louër

Denise Klein

Using speech samples as a biomarker is a promising avenue for detecting and monitoring the progression of Parkinson's disease (PD), but ther… (voir plus)e is considerable disagreement in the literature about how best to collect and analyze such data. Early research in detecting PD from speech used a sustained vowel phonation (SVP) task, while some recent research has explored recordings of more cognitively demanding tasks. To assess the role of language in PD detection, we tested pretrained models with varying data types and pretraining objectives and found that (1) text-only models match the performance of vocal-feature models, (2) multilingual Whisper outperforms self-supervised models whereas monolingual Whisper does worse, and (3) AudioSet pretraining improves performance on SVP but not spontaneous speech. These findings together highlight the critical role of language for the early detection of Parkinson's disease.

2025-07-01

arXiv (publié)

From Black Box to Biomarker: Sparse Autoencoders for Interpreting Speech Models of Parkinson's Disease

Peter William VanHarn Plantinga

Jen-Kai Chen

Roozbeh Sattari

Denise Klein

Speech holds promise as a cost-effective and non-invasive biomarker for neurological conditions such as Parkinson's disease (PD). While deep… (voir plus) learning systems trained on raw audio can find subtle signals not available from hand-crafted features, their black-box nature hinders clinical adoption. To address this, we apply sparse autoencoders (SAEs) to uncover interpretable internal representations from a speech-based PD detection system. We introduce a novel mask-based activation for adapting SAEs to small biomedical datasets, creating sparse disentangled dictionary representations. These dictionary entries are found to have strong associations with characteristic articulatory deficits in PD speech, such as reduced spectral flux and increased spectral flatness in the low-energy regions highlighted by the model attention. We further show that the spectral flux is related to volumetric measurements of the putamen from MRI scans, demonstrating the potential of SAEs to reveal clinically relevant biomarkers for disease monitoring and diagnosis.

2025-07-01

arXiv (publié)

Discrete Audio Tokens: More Than a Survey!

Pooneh Mousavi

Gallil Maimon

Adel Moumen

Darius Petermann

Jiatong Shi

Haibin Wu

Haici Yang

Anastasia Kuznetsova

Artem Ploujnikov

Ricard Marxer

Bhuvana Ramabhadran

Benjamin Elizalde

Loren Lugosch

Jinyu Li

Phil Woodland

Minje Kim

Hung-yi Lee

Shinji Watanabe

Yossi Adi … (voir 1 de plus)

Discrete audio tokens are compact representations that aim to preserve perceptual quality, phonetic content, and speaker characteristics whi… (voir plus)le enabling efficient storage and inference, as well as competitive performance across diverse downstream tasks.They provide a practical alternative to continuous features, enabling the integration of speech and audio into modern large language models (LLMs). As interest in token-based audio processing grows, various tokenization methods have emerged, and several surveys have reviewed the latest progress in the field. However, existing studies often focus on specific domains or tasks and lack a unified comparison across various benchmarks. This paper presents a systematic review and benchmark of discrete audio tokenizers, covering three domains: speech, music, and general audio. We propose a taxonomy of tokenization approaches based on encoder-decoder, quantization techniques, training paradigm, streamability, and application domains. We evaluate tokenizers on multiple benchmarks for reconstruction, downstream performance, and acoustic language modeling, and analyze trade-offs through controlled ablation studies. Our findings highlight key limitations, practical considerations, and open challenges, providing insight and guidance for future research in this rapidly evolving area. For more information, including our main results and tokenizer database, please refer to our website: https://poonehmousavi.github.io/dates-website/.

2025-06-12

ArXiv (prépublication)

Discrete Audio Tokens: More Than a Survey!

Pooneh Mousavi

Gallil Maimon

Adel Moumen

Darius Petermann

Jiatong Shi

Haibin Wu

Haici Yang

Anastasia Kuznetsova

Artem Ploujnikov

Ricard Marxer

Bhuvana Ramabhadran

Benjamin Elizalde

Loren Lugosch

Jinyu Li

Phil Woodland

Minje Kim

Hung-yi Lee

Shinji Watanabe

Yossi Adi … (voir 1 de plus)

Discrete audio tokens are compact representations that aim to preserve perceptual quality, phonetic content, and speaker characteristics whi… (voir plus)le enabling efficient storage and inference, as well as competitive performance across diverse downstream tasks. They provide a practical alternative to continuous features, enabling the integration of speech and audio into modern large language models (LLMs). As interest in token-based audio processing grows, various tokenization methods have emerged, and several surveys have reviewed the latest progress in the field. However, existing studies often focus on specific domains or tasks and lack a unified comparison across various benchmarks. This paper presents a systematic review and benchmark of discrete audio tokenizers, covering three domains: speech, music, and general audio. We propose a taxonomy of tokenization approaches based on encoder-decoder, quantization techniques, training paradigm, streamability, and application domains. We evaluate tokenizers on multiple benchmarks for reconstruction, downstream performance, and acoustic language modeling, and analyze trade-offs through controlled ablation studies. Our findings highlight key limitations, practical considerations, and open challenges, providing insight and guidance for future research in this rapidly evolving area. For more information, including our main results and tokenizer database, please refer to our website: https://poonehmousavi.github.io/dates-website/.

2025-06-12

ArXiv (prépublication)

LiSTEN: Learning Soft Token Embeddings for Neural Audio LLMs

Foundation models based on large language models (LLMs) have shown great success in handling various tasks and modalities. However, adapting… (voir plus) these models for general-purpose audio-language tasks is challenging due to differences in acoustic environments and task variations. In this work, we introduce LiSTEN Learning Soft Token Embeddings for Neural Audio LLMs), a framework for adapting LLMs to speech and audio tasks. LiSTEN uses a dynamic prompt selection strategy with learnable key-value pairs, allowing the model to balance general and task-specific knowledge while avoiding overfitting in a multitask setting. Our approach reduces dependence on large-scale ASR or captioning datasets, achieves competitive performance with fewer trainable parameters, and simplifies training by using a single-stage process. Additionally, LiSTEN enhances interpretability by analyzing the diversity and overlap of selected prompts across different tasks.

2025-05-24

ArXiv (prépublication)

Calm-Whisper: Reduce Whisper Hallucination On Non-Speech By Calming Crazy Heads Down

Yingzhi Wang

Anas Alhmoud

Saad Alsahly

Muhammad Alqurishi

2025-05-19

ArXiv (prépublication)

ALAS: Measuring Latent Speech-Text Alignment For Spoken Language Understanding In Multimodal LLMs

Pooneh Mousavi

Yingzhi Wang

2025-05-01

arXiv (publié)

Calm-Whisper: Reduce Whisper Hallucination On Non-Speech By Calming Crazy Heads Down

Yingzhi Wang

Anas Alhmoud

Saad Alsahly

Muhammad Alqurishi

2025-05-01

arXiv (publié)

LiSTEN: Learning Soft Token Embeddings for Neural Audio LLMs

2025-05-01

arXiv (publié)

Investigating the Effectiveness of Explainability Methods in Parkinson's Detection from Speech

Paolo Torroni

Speech impairments in Parkinson's disease (PD) provide significant early indicators for diagnosis. While models for speech-based PD detectio… (voir plus)n have shown strong performance, their interpretability remains underexplored. This study systematically evaluates several explainability methods to identify PD-specific speech features, aiming to support the development of accurate, interpretable models for clinical decision-making in PD diagnosis and monitoring. Our methodology involves (i) obtaining attributions and saliency maps using mainstream interpretability techniques, (ii) quantitatively evaluating the faithfulness of these maps and their combinations obtained via union and intersection through a range of established metrics, and (iii) assessing the information conveyed by the saliency maps for PD detection from an auxiliary classifier. Our results reveal that, while explanations are aligned with the classifier, they often fail to provide valuable information for domain experts.

2025-04-06

2025 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) (publié)