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Spoken Command Recognition
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논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
A Quantum Kernel Learning Approach to Acoustic Modeling for Spoken Command Recognition
An Ensemble Teacher-Student Learning Approach with Poisson Sub-sampling to Differential Privacy Preserving Speech Recognition
ATST: Audio Representation Learning with Teacher-Student Transformer
Exploiting Low-Rank Tensor-Train Deep Neural Networks Based on Riemannian Gradient Descent With Illustrations of Speech Processing
Exploiting Hybrid Models of Tensor-Train Networks for Spoken Command Recognition
SSAST: Self-Supervised Audio Spectrogram Transformer
Classical-to-Quantum Transfer Learning for Spoken Command Recognition Based on Quantum Neural Networks
Neural Model Reprogramming with Similarity Based Mapping for Low-Resource Spoken Command Recognition
Contrastive Learning of General-Purpose Audio Representations
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.