Search Results for author:
Found 38 papers, 28 papers with code
Learning Representations for New Sound Classes With Continual Self-Supervised Learning
In this paper, we present a self-supervised learning framework for continually learning representations for new sound classes.
On Using Transformers for Speech-Separation
In this paper, we extend our previous work by providing results on more datasets including LibriMix, and WHAM!, WHAMR!
OSSEM: one-shot speaker adaptive speech enhancement using meta learning
Although deep learning (DL) has achieved notable progress in speech enhancement (SE), further research is still required for a DL-based SE system to adapt effectively and efficiently to particular speakers.
REAL-M: Towards Speech Separation on Real Mixtures
First, we release the REAL-M dataset, a crowd-sourced corpus of real-life mixtures.
MetricGAN-U: Unsupervised speech enhancement/ dereverberation based only on noisy/ reverberated speech
Most of the deep learning-based speech enhancement models are learned in a supervised manner, which implies that pairs of noisy and clean speech are required during training.
Interpretable SincNet-based Deep Learning for Emotion Recognition from EEG brain activity
This result is consistent with recent neuroscience studies on emotion recognition, which found an association between these band suppressions and the behavioral deficits observed in individuals with ASD.
SpeechBrain: A General-Purpose Speech Toolkit
SpeechBrain is an open-source and all-in-one speech toolkit.
MetricGAN+: An Improved Version of MetricGAN for Speech Enhancement
The discrepancy between the cost function used for training a speech enhancement model and human auditory perception usually makes the quality of enhanced speech unsatisfactory.
Ranked #7 on
Speech Enhancement
on DEMAND
Timers and Such: A Practical Benchmark for Spoken Language Understanding with Numbers
This paper introduces Timers and Such, a new open source dataset of spoken English commands for common voice control use cases involving numbers.
ECAPA-TDNN Embeddings for Speaker Diarization
Learning robust speaker embeddings is a crucial step in speaker diarization.
Transformers with Competitive Ensembles of Independent Mechanisms
In this work we explore a way in which the Transformer architecture is deficient: it represents each position with a large monolithic hidden representation and a single set of parameters which are applied over the entire hidden representation.
Attention is All You Need in Speech Separation
Transformers are emerging as a natural alternative to standard RNNs, replacing recurrent computations with a multi-head attention mechanism.
Ranked #1 on
Speech Separation
on WSJ0-2mix
BIRD: Big Impulse Response Dataset
This paper introduces BIRD, the Big Impulse Response Dataset.
Sound Audio and Speech Processing
Quaternion Neural Networks for Multi-channel Distant Speech Recognition
In this paper, we propose to capture these inter- and intra- structural dependencies with quaternion neural networks, which can jointly process multiple signals as whole quaternion entities.
Multi-task self-supervised learning for Robust Speech Recognition
We then propose a revised encoder that better learns short- and long-term speech dynamics with an efficient combination of recurrent and convolutional networks.
Using Speech Synthesis to Train End-to-End Spoken Language Understanding Models
End-to-end models are an attractive new approach to spoken language understanding (SLU) in which the meaning of an utterance is inferred directly from the raw audio without employing the standard pipeline composed of a separately trained speech recognizer and natural language understanding module.
Retrieving Signals in the Frequency Domain with Deep Complex Extractors
Using the Wall Street Journal Dataset, we compare our phase-aware loss to several others that operate both in the time and frequency domains and demonstrate the effectiveness of our proposed signal extraction method and proposed loss.
Retrieving Signals with Deep Complex Extractors
Building on recent advances, we propose a new deep complex-valued method for signal retrieval and extraction in the frequency domain.
Speech Model Pre-training for End-to-End Spoken Language Understanding
Whereas conventional spoken language understanding (SLU) systems map speech to text, and then text to intent, end-to-end SLU systems map speech directly to intent through a single trainable model.
Ranked #2 on
Spoken Language Understanding
on Fluent Speech Commands
(using extra training data)
Learning Problem-agnostic Speech Representations from Multiple Self-supervised Tasks
Learning good representations without supervision is still an open issue in machine learning, and is particularly challenging for speech signals, which are often characterized by long sequences with a complex hierarchical structure.
Ranked #2 on
Distant Speech Recognition
on DIRHA English WSJ
Speech and Speaker Recognition from Raw Waveform with SincNet
Deep neural networks can learn complex and abstract representations, that are progressively obtained by combining simpler ones.
Learning Speaker Representations with Mutual Information
Mutual Information (MI) or similar measures of statistical dependence are promising tools for learning these representations in an unsupervised way.
Interpretable Convolutional Filters with SincNet
Deep learning is currently playing a crucial role toward higher levels of artificial intelligence.
Ranked #3 on
Distant Speech Recognition
on DIRHA English WSJ
Speech recognition with quaternion neural networks
Neural network architectures are at the core of powerful automatic speech recognition systems (ASR).
The PyTorch-Kaldi Speech Recognition Toolkit
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.
Ranked #1 on
Distant Speech Recognition
on DIRHA English WSJ
Speaker Recognition from Raw Waveform with SincNet
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.
Quaternion Recurrent Neural Networks
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 of a sequence.
Automatic context window composition for distant speech recognition
Distant speech recognition is being revolutionized by deep learning, that has contributed to significantly outperform previous HMM-GMM systems.
Twin Regularization for online speech recognition
Online speech recognition is crucial for developing natural human-machine interfaces.
Light Gated Recurrent Units for Speech Recognition
A field that has directly benefited from the recent advances in deep learning is Automatic Speech Recognition (ASR).
Ranked #6 on
Speech Recognition
on TIMIT
Deep Learning for Distant Speech Recognition
Deep learning is an emerging technology that is considered one of the most promising directions for reaching higher levels of artificial intelligence.
Realistic multi-microphone data simulation for distant speech recognition
The availability of realistic simulated corpora is of key importance for the future progress of distant speech recognition technology.
Audio and Speech Processing Sound
Contaminated speech training methods for robust DNN-HMM distant speech recognition
Despite the significant progress made in the last years, state-of-the-art speech recognition technologies provide a satisfactory performance only in the close-talking condition.
The DIRHA-English corpus and related tasks for distant-speech recognition in domestic environments
This paper introduces the contents and the possible usage of the DIRHA-ENGLISH multi-microphone corpus, recently realized under the EC DIRHA project.
Improving speech recognition by revising gated recurrent units
First, we suggest to remove the reset gate in the GRU design, resulting in a more efficient single-gate architecture.
Batch-normalized joint training for DNN-based distant speech recognition
Improving distant speech recognition is a crucial step towards flexible human-machine interfaces.
A network of deep neural networks for distant speech recognition
Despite the remarkable progress recently made in distant speech recognition, state-of-the-art technology still suffers from a lack of robustness, especially when adverse acoustic conditions characterized by non-stationary noises and reverberation are met.
The DIRHA simulated corpus
This paper describes a multi-microphone multi-language acoustic corpus being developed under the EC project Distant-speech Interaction for Robust Home Applications (DIRHA).
