Search Results for author:
Found 29 papers, 13 papers with code
Federated Learning with Differential Privacy for End-to-End Speech Recognition
($4. 5$, $10^{-9}$)-$\textbf{DP}$) with a 1. 3% (resp.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
AV-CPL: Continuous Pseudo-Labeling for Audio-Visual Speech Recognition
Audio-visual speech contains synchronized audio and visual information that provides cross-modal supervision to learn representations for both automatic speech recognition (ASR) and visual speech recognition (VSR).
Audio-Visual Speech Recognition
Automatic Speech Recognition
+4
Importance of Smoothness Induced by Optimizers in FL4ASR: Towards Understanding Federated Learning for End-to-End ASR
In this paper, we start by training End-to-End Automatic Speech Recognition (ASR) models using Federated Learning (FL) and examining the fundamental considerations that can be pivotal in minimizing the performance gap in terms of word error rate between models trained using FL versus their centralized counterpart.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
VISION Datasets: A Benchmark for Vision-based InduStrial InspectiON
We introduce the VISION Datasets, a diverse collection of 14 industrial inspection datasets, uniquely poised to meet these challenges.
Unsupervised ASR via Cross-Lingual Pseudo-Labeling
Here, "unsupervised" means no labeled audio is available for the $\textit{target}$ language.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Stabilizing Transformer Training by Preventing Attention Entropy Collapse
We show that $\sigma$Reparam provides stability and robustness with respect to the choice of hyperparameters, going so far as enabling training (a) a Vision Transformer {to competitive performance} without warmup, weight decay, layer normalization or adaptive optimizers; (b) deep architectures in machine translation and (c) speech recognition to competitive performance without warmup and adaptive optimizers.
Joint Speech Transcription and Translation: Pseudo-Labeling with Out-of-Distribution Data
Self-training has been shown to be helpful in addressing data scarcity for many domains, including vision, speech, and language.
Continuous Soft Pseudo-Labeling in ASR
Continuous pseudo-labeling (PL) algorithms such as slimIPL have recently emerged as a powerful strategy for semi-supervised learning in speech recognition.
More Speaking or More Speakers?
We perform a systematic analysis on both labeled and unlabeled data by varying the number of speakers while keeping the number of hours fixed and vice versa.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Continuous Pseudo-Labeling from the Start
Nevertheless, these approaches still rely on bootstrapping the ST using an initial supervised learning phase where the model is trained on labeled data alone.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Position Prediction as an Effective Pretraining Strategy
This pretraining strategy which has been used in BERT models in NLP, Wav2Vec models in Speech and, recently, in MAE models in Vision, forces the model to learn about relationships between the content in different parts of the input using autoencoding related objectives.
Flashlight: Enabling Innovation in Tools for Machine Learning
This is in part due to the difficulties involved in prototyping new computational paradigms with existing frameworks.
Pseudo-Labeling for Massively Multilingual Speech Recognition
Semi-supervised learning through pseudo-labeling has become a staple of state-of-the-art monolingual speech recognition systems.
Word Order Does Not Matter For Speech Recognition
In this paper, we study training of automatic speech recognition system in a weakly supervised setting where the order of words in transcript labels of the audio training data is not known.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Kaizen: Continuously improving teacher using Exponential Moving Average for semi-supervised speech recognition
In this paper, we introduce the Kaizen framework that uses a continuously improving teacher to generate pseudo-labels for semi-supervised speech recognition (ASR).
CAPE: Encoding Relative Positions with Continuous Augmented Positional Embeddings
Absolute or relative positional embeddings are the most popular ways to feed Transformer models with positional information.
Robust wav2vec 2.0: Analyzing Domain Shift in Self-Supervised Pre-Training
On a large-scale competitive setup, we show that pre-training on unlabeled in-domain data reduces the gap between models trained on in-domain and out-of-domain labeled data by 66%-73%.
Joint Masked CPC and CTC Training for ASR
Self-supervised learning (SSL) has shown promise in learning representations of audio that are useful for automatic speech recognition (ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
SlimIPL: Language-Model-Free Iterative Pseudo-Labeling
We improve upon the IPL algorithm: as the model learns, we propose to iteratively re-generate transcriptions with hard labels (the most probable tokens), that is, without a language model.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Self-training and Pre-training are Complementary for Speech Recognition
Self-training and unsupervised pre-training have emerged as effective approaches to improve speech recognition systems using unlabeled data.
Ranked #1 on
Speech Recognition
on LibriSpeech train-clean-100 test-other
(using extra training data)
Rethinking Evaluation in ASR: Are Our Models Robust Enough?
Finally, we show that training a single acoustic model on the most widely-used datasets - combined - reaches competitive performance on both research and real-world benchmarks.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Iterative Pseudo-Labeling for Speech Recognition
In particular, IPL fine-tunes an existing model at each iteration using both labeled data and a subset of unlabeled data.
Ranked #11 on
Speech Recognition
on LibriSpeech test-other
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Scaling Up Online Speech Recognition Using ConvNets
We design an online end-to-end speech recognition system based on Time-Depth Separable (TDS) convolutions and Connectionist Temporal Classification (CTC).
Libri-Light: A Benchmark for ASR with Limited or No Supervision
Additionally, we provide baseline systems and evaluation metrics working under three settings: (1) the zero resource/unsupervised setting (ABX), (2) the semi-supervised setting (PER, CER) and (3) the distant supervision setting (WER).
Ranked #1 on
Speech Recognition
on Libri-Light test-other
(ABX-within metric)
End-to-end ASR: from Supervised to Semi-Supervised Learning with Modern Architectures
We study pseudo-labeling for the semi-supervised training of ResNet, Time-Depth Separable ConvNets, and Transformers for speech recognition, with either CTC or Seq2Seq loss functions.
Ranked #16 on
Speech Recognition
on LibriSpeech test-other
(using extra training data)
Who Needs Words? Lexicon-Free Speech Recognition
Lexicon-free speech recognition naturally deals with the problem of out-of-vocabulary (OOV) words.
InfiniteBoost: building infinite ensembles with gradient descent
In machine learning ensemble methods have demonstrated high accuracy for the variety of problems in different areas.
Inclusive Flavour Tagging Algorithm
The proposed inclusive flavour-tagging algorithm is applicable to tag the flavour of $B$ mesons in any proton-proton experiment.
Reproducible Experiment Platform
Data analysis in fundamental sciences nowadays is an essential process that pushes frontiers of our knowledge and leads to new discoveries.
Data Analysis, Statistics and Probability
