106 citations · 212 across the 9 of their papers we have counts for
11 papers
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…
CGCNN: Complex Gabor Convolutional Neural Network on raw speech
Paul-Gauthier Noé, Titouan Parcollet, Mohamed Morchid
Convolutional Neural Networks (CNN) have been used in Automatic Speech Recognition (ASR) to learn representations directly from the raw signal instead of hand-crafted acoustic feat…
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…
M2H-GAN: A GAN-based Mapping from Machine to Human Transcripts for Speech Understanding
Titouan Parcollet, Mohamed Morchid, Xavier Bost +1
Deep learning is at the core of recent spoken language understanding (SLU) related tasks. More precisely, deep neural networks (DNNs) drastically increased the performances of SLU…
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…
Quaternion Convolutional Neural Networks for Heterogeneous Image Processing
Titouan Parcollet, Mohamed Morchid, Georges Linarès
Convolutional neural networks (CNN) have recently achieved state-of-the-art results in various applications. In the case of image recognition, an ideal model has to learn independe…
Quaternion Convolutional Neural Networks for End-to-End Automatic Speech Recognition
Titouan Parcollet, Ying Zhang, Mohamed Morchid +4
Recently, the connectionist temporal classification (CTC) model coupled with recurrent (RNN) or convolutional neural networks (CNN), made it easier to train speech recognition syst…
Quaternion Recurrent Neural Networks
Titouan Parcollet, Mirco Ravanelli, Mohamed Morchid +4
Recurrent neural networks (RNNs) are powerful architectures to model sequential data, due to their capability to learn short and long-term dependencies between the basic elements o…
Automatic Text Summarization Approaches to Speed up Topic Model Learning Process
Mohamed Morchid, Juan-Manuel Torres-Moreno, Richard Dufour +2
The number of documents available into Internet moves each day up. For this reason, processing this amount of information effectively and expressibly becomes a major concern for co…
Parallel Long Short-Term Memory for Multi-stream Classification
Mohamed Bouaziz, Mohamed Morchid, Richard Dufour +2
Recently, machine learning methods have provided a broad spectrum of original and efficient algorithms based on Deep Neural Networks (DNN) to automatically predict an outcome with…