Search Results for author:
Found 17 papers, 13 papers with code
Improving Multilingual ASR in the Wild Using Simple N-best Re-ranking
Multilingual Automatic Speech Recognition (ASR) models are typically evaluated in a setting where the ground-truth language of the speech utterance is known, however, this is often not the case for most practical settings.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Scaling A Simple Approach to Zero-Shot Speech Recognition
Despite rapid progress in increasing the language coverage of automatic speech recognition, the field is still far from covering all languages with a known writing script.
Less Peaky and More Accurate CTC Forced Alignment by Label Priors
Connectionist temporal classification (CTC) models are known to have peaky output distributions.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
TorchAudio 2.1: Advancing speech recognition, self-supervised learning, and audio processing components for PyTorch
TorchAudio is an open-source audio and speech processing library built for PyTorch.
Scaling Speech Technology to 1,000+ Languages
Expanding the language coverage of speech technology has the potential to improve access to information for many more people.
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.
Star Temporal Classification: Sequence Classification with Partially Labeled Data
These experiments show that STC can recover most of the performance of supervised baseline when up to 70% of the labels are missing.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
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
Parallel Composition of Weighted Finite-State Transducers
We benchmark our parallel algorithm on the composition of random graphs and the composition of graphs commonly used in speech recognition.
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%.
MLS: A Large-Scale Multilingual Dataset for Speech Research
This paper introduces Multilingual LibriSpeech (MLS) dataset, a large multilingual corpus suitable for speech research.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
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
Differentiable Weighted Finite-State Transducers
We introduce a framework for automatic differentiation with weighted finite-state transducers (WFSTs) allowing them to be used dynamically at training time.
Massively Multilingual ASR: 50 Languages, 1 Model, 1 Billion Parameters
We study training a single acoustic model for multiple languages with the aim of improving automatic speech recognition (ASR) performance on low-resource languages, and over-all simplifying deployment of ASR systems that support diverse languages.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
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).
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 #19 on
Speech Recognition
on LibriSpeech test-other
(using extra training data)
wav2letter++: The Fastest Open-source Speech Recognition System
This paper introduces wav2letter++, the fastest open-source deep learning speech recognition framework.
