Spoken Command Recognition
5 papers with code • 1 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in Spoken Command Recognition
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Most implemented papers
ATST: Audio Representation Learning with Teacher-Student Transformer
Self-supervised learning (SSL) learns knowledge from a large amount of unlabeled data, and then transfers the knowledge to a specific problem with a limited number of labeled data.
Contrastive Learning of General-Purpose Audio Representations
google-research/google-research
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We introduce COLA, a self-supervised pre-training approach for learning a general-purpose representation of audio.
SSAST: Self-Supervised Audio Spectrogram Transformer
YuanGongND/ssast
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However, pure Transformer models tend to require more training data compared to CNNs, and the success of the AST relies on supervised pretraining that requires a large amount of labeled data and a complex training pipeline, thus limiting the practical usage of AST.
Neural Model Reprogramming with Similarity Based Mapping for Low-Resource Spoken Command Recognition
dodohow1011/speechadvreprogram
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In this study, we propose a novel adversarial reprogramming (AR) approach for low-resource spoken command recognition (SCR), and build an AR-SCR system.
Exploiting Low-Rank Tensor-Train Deep Neural Networks Based on Riemannian Gradient Descent With Illustrations of Speech Processing
uwjunqi/ttn-vqc
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This work focuses on designing low complexity hybrid tensor networks by considering trade-offs between the model complexity and practical performance.
