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Found 14 papers, 9 papers with code
CL-MASR: A Continual Learning Benchmark for Multilingual ASR
To the best of our knowledge, CL-MASR is the first continual learning benchmark for the multilingual ASR task.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Big model only for hard audios: Sample dependent Whisper model selection for efficient inferences
Recent progress in Automatic Speech Recognition (ASR) has been coupled with a substantial increase in the model sizes, which may now contain billions of parameters, leading to slow inferences even with adapted hardware.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Leveraging Data Collection and Unsupervised Learning for Code-switched Tunisian Arabic Automatic Speech Recognition
Crafting an effective Automatic Speech Recognition (ASR) solution for dialects demands innovative approaches that not only address the data scarcity issue but also navigate the intricacies of linguistic diversity.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Training dynamic models using early exits for automatic speech recognition on resource-constrained devices
In self-attention models for automatic speech recognition (ASR), early-exit architectures enable the development of dynamic models capable of adapting their size and architecture to varying levels of computational resources and ASR performance demands.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
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.
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.
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.
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
DP-Parse: Finding Word Boundaries from Raw Speech with an Instance Lexicon
Finding word boundaries in continuous speech is challenging as there is little or no equivalent of a 'space' delimiter between words.
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.
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
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
Sequence to Sequence Learning for Query Expansion
Using sequence to sequence algorithms for query expansion has not been explored yet in Information Retrieval literature nor in Question-Answering's.
