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Found 12 papers, 1 papers with code
Improving Small Footprint Few-shot Keyword Spotting with Supervision on Auxiliary Data
Few-shot keyword spotting (FS-KWS) models usually require large-scale annotated datasets to generalize to unseen target keywords.
Scalable Weight Reparametrization for Efficient Transfer Learning
Efficient transfer learning involves utilizing a pre-trained model trained on a larger dataset and repurposing it for downstream tasks with the aim of maximizing the reuse of the pre-trained model.
TTN: A Domain-Shift Aware Batch Normalization in Test-Time Adaptation
In this paper, we identify that CBN and TBN are in a trade-off relationship and present a new test-time normalization (TTN) method that interpolates the statistics by adjusting the importance between CBN and TBN according to the domain-shift sensitivity of each BN layer.
QTI Submission to DCASE 2021: residual normalization for device-imbalanced acoustic scene classification with efficient design
The goal of the task is to design an audio scene classification system for device-imbalanced datasets under the constraints of model complexity.
Personalized Keyword Spotting through Multi-task Learning
We design two personalized KWS tasks; (1) Target user Biased KWS (TB-KWS) and (2) Target user Only KWS (TO-KWS).
Dummy Prototypical Networks for Few-Shot Open-Set Keyword Spotting
We also verify our method on a standard benchmark, miniImageNet, and D-ProtoNets shows the state-of-the-art open-set detection rate in FSOSR.
Domain Generalization with Relaxed Instance Frequency-wise Normalization for Multi-device Acoustic Scene Classification
While using two-dimensional convolutional neural networks (2D-CNNs) in image processing, it is possible to manipulate domain information using channel statistics, and instance normalization has been a promising way to get domain-invariant features.
Domain Generalization on Efficient Acoustic Scene Classification using Residual Normalization
Moreover, we introduce an efficient architecture, BC-ResNet-ASC, a modified version of the baseline architecture with a limited receptive field.
Towards Robust Domain Generalization in 2D Neural Audio Processing
While using two-dimensional convolutional neural networks (2D-CNNs) in image processing, it is possible to manipulate domain information using channel statistics, and instance normalization has been a promising way to get domain-invariant features.
Broadcasted Residual Learning for Efficient Keyword Spotting
We present a broadcasted residual learning method to achieve high accuracy with small model size and computational load.
Ranked #2 on
Keyword Spotting
on Google Speech Commands
Query-by-example on-device keyword spotting
A keyword spotting (KWS) system determines the existence of, usually predefined, keyword in a continuous speech stream.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Orthogonality Constrained Multi-Head Attention For Keyword Spotting
Augmenting regularization terms which penalize positional and contextual non-orthogonality between the attention heads encourages to output different representations from separate subsequences, which in turn enables leveraging structured information without explicit sequence models such as hidden Markov models.
