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Found 35 papers, 19 papers with code
Enhancing expressivity transfer in textless speech-to-speech translation
Textless speech-to-speech translation systems are rapidly advancing, thanks to the integration of self-supervised learning techniques.
LeBenchmark 2.0: a Standardized, Replicable and Enhanced Framework for Self-supervised Representations of French Speech
Self-supervised learning (SSL) is at the origin of unprecedented improvements in many different domains including computer vision and natural language processing.
Speech Self-Supervised Representations Benchmarking: a Case for Larger Probing Heads
Self-supervised learning (SSL) leverages large datasets of unlabeled speech to reach impressive performance with reduced amounts of annotated data.
SummaryMixing: A Linear-Complexity Alternative to Self-Attention for Speech Recognition and Understanding
Unfortunately, token mixing with self-attention takes quadratic time in the length of the speech utterance, slowing down inference as well as training and increasing memory consumption.
Learning Multilingual Expressive Speech Representation for Prosody Prediction without Parallel Data
We propose a method for speech-to-speech emotionpreserving translation that operates at the level of discrete speech units.
Speech Self-Supervised Representation Benchmarking: Are We Doing it Right?
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 of annotated data.
Automatic Data Augmentation for Domain Adapted Fine-Tuning of Self-Supervised Speech Representations
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 datasets.
HyperConformer: Multi-head HyperMixer for Efficient Speech Recognition
In particular, multi-head HyperConformer achieves comparable or higher recognition performance while being more efficient than Conformer in terms of inference speed, memory, parameter count, and available training data.
Fine-tuning Strategies for Faster Inference using Speech Self-Supervised Models: A Comparative Study
Self-supervised learning (SSL) has allowed substantial progress in Automatic Speech Recognition (ASR) performance in low-resource settings.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Stabilising and accelerating light gated recurrent units for automatic speech recognition
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 standard gated recurrent units (GRU).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Match to Win: Analysing Sequences Lengths for Efficient Self-supervised Learning in Speech and Audio
Self-supervised learning (SSL) has proven vital in speech and audio-related applications.
ZeroFL: Efficient On-Device Training for Federated Learning with Local Sparsity
When the available hardware cannot meet the memory and compute requirements to efficiently train high performing machine learning models, a compromise in either the training quality or the model complexity is needed.
Automatic Data Augmentation Selection and Parametrization in Contrastive Self-Supervised Speech Representation Learning
Thus, this work introduces a conditional independance-based method which allows for automatically selecting a suitable distribution on the choice of augmentations and their parametrization from a set of predefined ones, for contrastive self-supervised pre-training.
Federated Self-supervised Speech Representations: Are We There Yet?
The ubiquity of microphone-enabled devices has lead to large amounts of unlabelled audio data being produced at the edge.
End-to-end model for named entity recognition from speech without paired training data
Our approach is based on the use of an external model trained to generate a sequence of vectorial representations from text.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
Pretext Tasks selection for multitask self-supervised speech representation learning
Through solving pretext tasks, self-supervised learning leverages unlabeled data to extract useful latent representations replacing traditional input features in the downstream task.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
SpeechBrain: A General-Purpose Speech Toolkit
SpeechBrain is an open-source and all-in-one speech toolkit.
LeBenchmark: A Reproducible Framework for Assessing Self-Supervised Representation Learning from Speech
In this paper, we propose LeBenchmark: a reproducible framework for assessing SSL from speech.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+6
Conditional independence for pretext task selection in Self-supervised speech representation learning
Through solving pretext tasks, self-supervised learning (SSL) leverages unlabeled data to extract useful latent representations replacing traditional input features in the downstream task.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
On-device Federated Learning with Flower
Federated Learning (FL) allows edge devices to collaboratively learn a shared prediction model while keeping their training data on the device, thereby decoupling the ability to do machine learning from the need to store data in the cloud.
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.
Ranked #4 on
Spoken Language Understanding
on Timers and Such
(using extra training data)
A first look into the carbon footprint of federated learning
Despite impressive results, deep learning-based technologies also raise severe privacy and environmental concerns induced by the training procedure often conducted in data centers.
Adversarial Disentanglement of Speaker Representation for Attribute-Driven Privacy Preservation
In order to allow the user to choose which information to protect, we introduce in this paper the concept of attribute-driven privacy preservation in speaker voice representation.
Can Federated Learning Save The Planet?
Then, we compare the carbon footprint of FL to traditional centralized learning.
Flower: A Friendly Federated Learning Research Framework
Federated Learning (FL) has emerged as a promising technique for edge devices to collaboratively learn a shared prediction model, while keeping their training data on the device, thereby decoupling the ability to do machine learning from the need to store the data in the cloud.
Distilling Knowledge from Ensembles of Acoustic Models for Joint CTC-Attention End-to-End Speech Recognition
In the specific context of Automatic Speech Recognition (ASR), distillation from ensembles of acoustic models has recently shown promising results in increasing recognition performance.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
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.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Real to H-space Encoder for Speech Recognition
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 input sequences.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
M2H-GAN: A GAN-based Mapping from Machine to Human Transcripts for Speech Understanding
TRS transcripts are only used to measure the performances of ASR systems.
Generative Adversarial Network
Spoken Language Understanding
Speech recognition with quaternion neural networks
Neural network architectures are at the core of powerful automatic speech recognition systems (ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
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
Bidirectional Quaternion Long-Short Term Memory Recurrent Neural Networks for Speech Recognition
Recurrent neural networks (RNN) are at the core of modern automatic speech recognition (ASR) systems.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Quaternion Convolutional Neural Networks for End-to-End Automatic Speech Recognition
Quaternion numbers and quaternion neural networks have shown their efficiency to process multidimensional inputs as entities, to encode internal dependencies, and to solve many tasks with less learning parameters than real-valued models.
Ranked #19 on
Speech Recognition
on TIMIT
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
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 Speech Recognition
Automatic Speech Recognition (ASR)
+1
