Small-Footprint Keyword Spotting
7 papers with code • 0 benchmarks • 0 datasets
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Latest papers with no code
DCCRN-KWS: an audio bias based model for noise robust small-footprint keyword spotting
Real-world complex acoustic environments especially the ones with a low signal-to-noise ratio (SNR) will bring tremendous challenges to a keyword spotting (KWS) system.
Small-footprint slimmable networks for keyword spotting
In this work, we present Slimmable Neural Networks applied to the problem of small-footprint keyword spotting.
Exploring Representation Learning for Small-Footprint Keyword Spotting
To address those challenges, we explore representation learning for KWS by self-supervised contrastive learning and self-training with pretrained model.
Filterbank Learning for Noise-Robust Small-Footprint Keyword Spotting
In the context of keyword spotting (KWS), the replacement of handcrafted speech features by learnable features has not yielded superior KWS performance.
Text Anchor Based Metric Learning for Small-footprint Keyword Spotting
Recently proposed metric learning approaches improved the generalizability of models for the KWS task, and 1D-CNN based KWS models have achieved the state-of-the-arts (SOTA) in terms of model size.
AUC Optimization for Robust Small-footprint Keyword Spotting with Limited Training Data
Deep neural networks provide effective solutions to small-footprint keyword spotting (KWS).
Small-footprint Keyword Spotting with Graph Convolutional Network
Despite the recent successes of deep neural networks, it remains challenging to achieve high precision keyword spotting task (KWS) on resource-constrained devices.
A Monaural Speech Enhancement Method for Robust Small-Footprint Keyword Spotting
To improve the robustness, a speech enhancement front-end is involved.
Low-bit quantization and quantization-aware training for small-footprint keyword spotting
We investigate low-bit quantization to reduce computational cost of deep neural network (DNN) based keyword spotting (KWS).
Streaming Small-Footprint Keyword Spotting using Sequence-to-Sequence Models
We develop streaming keyword spotting systems using a recurrent neural network transducer (RNN-T) model: an all-neural, end-to-end trained, sequence-to-sequence model which jointly learns acoustic and language model components.
