SOTAVerified

Spoken Command Recognition

Papers

Showing 1–10 of 10 papers

TitleStatusHype
Variational Bayesian Adaptive Learning of Deep Latent Variables for Acoustic Knowledge Transfer—0
A Quantum Kernel Learning Approach to Acoustic Modeling for Spoken Command Recognition—0
An Ensemble Teacher-Student Learning Approach with Poisson Sub-sampling to Differential Privacy Preserving Speech Recognition—0
ATST: Audio Representation Learning with Teacher-Student TransformerCode1
Exploiting Low-Rank Tensor-Train Deep Neural Networks Based on Riemannian Gradient Descent With Illustrations of Speech ProcessingCode0
Exploiting Hybrid Models of Tensor-Train Networks for Spoken Command Recognition—0
SSAST: Self-Supervised Audio Spectrogram TransformerCode2
Classical-to-Quantum Transfer Learning for Spoken Command Recognition Based on Quantum Neural Networks—0
Neural Model Reprogramming with Similarity Based Mapping for Low-Resource Spoken Command RecognitionCode1
Contrastive Learning of General-Purpose Audio RepresentationsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SSAST-FRAMEAccuracy98.1—Unverified
2Base (ours)Accuracy98—Unverified
3SSAST-PATCHAccuracy98—Unverified
4COLAAccuracy95.5—Unverified