514 citations · 918 across the 31 of their papers we have counts for
16 papers · 1 filter
Speech Self-Supervised Representations Benchmarking: a Case for Larger Probing Heads
Salah Zaiem, Youcef Kemiche, Titouan Parcollet +2
Self-supervised learning (SSL) leverages large datasets of unlabeled speech to reach impressive performance with reduced amounts of annotated data. The high number of proposed appr…
Learning Multilingual Expressive Speech Representation for Prosody Prediction without Parallel Data
Jarod Duret, Titouan Parcollet, Yannick Estève
We propose a method for speech-to-speech emotionpreserving translation that operates at the level of discrete speech units. Our approach relies on the use of multilingual emotion e…
Automatic Data Augmentation for Domain Adapted Fine-Tuning of Self-Supervised Speech Representations
Salah Zaiem, Titouan Parcollet, Slim Essid
Self-Supervised Learning (SSL) has allowed leveraging large amounts of unlabeled speech data to improve the performance of speech recognition models even with small annotated datas…
Speech Self-Supervised Representation Benchmarking: Are We Doing it Right?
Salah Zaiem, Youcef Kemiche, Titouan Parcollet +2
Self-supervised learning (SSL) has recently allowed leveraging large datasets of unlabeled speech signals to reach impressive performance on speech tasks using only small amounts o…
Fine-tuning Strategies for Faster Inference using Speech Self-Supervised Models: A Comparative Study
Salah Zaiem, Robin Algayres, Titouan Parcollet +2
Self-supervised learning (SSL) has allowed substantial progress in Automatic Speech Recognition (ASR) performance in low-resource settings. In this context, it has been demonstrate…
Stabilising and accelerating light gated recurrent units for automatic speech recognition
Adel Moumen, Titouan Parcollet
The light gated recurrent units (Li-GRU) is well-known for achieving impressive results in automatic speech recognition (ASR) tasks while being lighter and faster to train than a s…
Automatic Data Augmentation Selection and Parametrization in Contrastive Self-Supervised Speech Representation Learning
Salah Zaiem, Titouan Parcollet, Slim Essid
Contrastive learning enables learning useful audio and speech representations without ground-truth labels by maximizing the similarity between latent representations of similar sig…
Pretext Tasks selection for multitask self-supervised speech representation learning
Salah Zaiem, Titouan Parcollet, Slim Essid +1
Through solving pretext tasks, self-supervised learning leverages unlabeled data to extract useful latent representations replacing traditional input features in the downstream tas…
SpeechBrain: A General-Purpose Speech Toolkit
Mirco Ravanelli, Titouan Parcollet, Peter Plantinga +18
SpeechBrain is an open-source and all-in-one speech toolkit. It is designed to facilitate the research and development of neural speech processing technologies by being simple, fle…
Conditional independence for pretext task selection in Self-supervised speech representation learning
Salah Zaiem, Titouan Parcollet, Slim Essid
Through solving pretext tasks, self-supervised learning (SSL) leverages unlabeled data to extract useful latent representations replacing traditional input features in the downstre…
Adversarial Disentanglement of Speaker Representation for Attribute-Driven Privacy Preservation
Paul-Gauthier Noé, Mohammad Mohammadamini, Driss Matrouf +3
In speech technologies, speaker's voice representation is used in many applications such as speech recognition, voice conversion, speech synthesis and, obviously, user authenticati…
Quaternion Neural Networks for Multi-channel Distant Speech Recognition
Xinchi Qiu, Titouan Parcollet, Mirco Ravanelli +2
Despite the significant progress in automatic speech recognition (ASR), distant ASR remains challenging due to noise and reverberation. A common approach to mitigate this issue con…
Real to H-space Encoder for Speech Recognition
Titouan Parcollet, Mohamed Morchid, Georges Linarès +1
Deep neural networks (DNNs) and more precisely recurrent neural networks (RNNs) are at the core of modern automatic speech recognition systems, due to their efficiency to process i…
Speech recognition with quaternion neural networks
Titouan Parcollet, Mirco Ravanelli, Mohamed Morchid +2
Neural network architectures are at the core of powerful automatic speech recognition systems (ASR). However, while recent researches focus on novel model architectures, the acoust…
Bidirectional Quaternion Long-Short Term Memory Recurrent Neural Networks for Speech Recognition
Titouan Parcollet, Mohamed Morchid, Georges Linarès +1
Recurrent neural networks (RNN) are at the core of modern automatic speech recognition (ASR) systems. In particular, long-short term memory (LSTM) recurrent neural networks have ac…
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, for instance, is nowadays an established…