Papers Spoken Command Recognition
“Spoken Command Recognition” 태그가 달린 논문 10편 · 필터 해제
Variational Bayesian Adaptive Learning of Deep Latent Variables for Acoustic Knowledge Transfer
In this work, we propose a novel variational Bayesian adaptive learning approach for cross-domain knowledge transfer to address acoustic mismatches between training and testing conditions, such as recording devices and e…
Acoustic Scene ClassificationScene ClassificationSpoken Command RecognitionTransfer Learning+1A Quantum Kernel Learning Approach to Acoustic Modeling for Spoken Command Recognition
We propose a quantum kernel learning (QKL) framework to address the inherent data sparsity issues often encountered in training large-scare acoustic models in low-resource scenarios. We project acoustic features based on…
Spoken Command RecognitionAn Ensemble Teacher-Student Learning Approach with Poisson Sub-sampling to Differential Privacy Preserving Speech Recognition
We propose an ensemble learning framework with Poisson sub-sampling to effectively train a collection of teacher models to issue some differential privacy (DP) guarantee for training data. Through boosting under DP, a st…
Ensemble LearningPrivacy Preservingspeech-recognitionSpeech Recognition+1ATST: 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. SSL has achieved promising results in v…
Audio ClassificationInstrument RecognitionRepresentation LearningSelf-Supervised Audio Classification+3Exploiting Low-Rank Tensor-Train Deep Neural Networks Based on Riemannian Gradient Descent With Illustrations of Speech Processing
This work focuses on designing low complexity hybrid tensor networks by considering trade-offs between the model complexity and practical performance. Firstly, we exploit a low-rank tensor-train deep neural network (TT-D…
Speech EnhancementSpoken Command RecognitionTensor NetworksExploiting Hybrid Models of Tensor-Train Networks for Spoken Command Recognition
This work aims to design a low complexity spoken command recognition (SCR) system by considering different trade-offs between the number of model parameters and classification accuracy. More specifically, we exploit a de…
Spoken Command RecognitionSSAST: Self-Supervised Audio Spectrogram Transformer
Recently, neural networks based purely on self-attention, such as the Vision Transformer (ViT), have been shown to outperform deep learning models constructed with convolutional neural networks (CNNs) on various vision t…
Audio ClassificationClassificationEmotion RecognitionKeyword Spotting+3Classical-to-Quantum Transfer Learning for Spoken Command Recognition Based on Quantum Neural Networks
This work investigates an extension of transfer learning applied in machine learning algorithms to the emerging hybrid end-to-end quantum neural network (QNN) for spoken command recognition (SCR). Our QNN-based SCR syste…
Spoken Command RecognitionTransfer LearningNeural Model Reprogramming with Similarity Based Mapping for Low-Resource Spoken Command Recognition
In this study, we propose a novel adversarial reprogramming (AR) approach for low-resource spoken command recognition (SCR), and build an AR-SCR system. The AR procedure aims to modify the acoustic signals (from the targ…
Spoken Command RecognitionTransfer LearningContrastive Learning of General-Purpose Audio Representations
We introduce COLA, a self-supervised pre-training approach for learning a general-purpose representation of audio. Our approach is based on contrastive learning: it learns a representation which assigns high similarity t…
CoLAContrastive LearningSpeaker IdentificationSpoken Command Recognition