Small-Footprint Keyword Spotting
7 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in Small-Footprint Keyword Spotting
Most implemented papers
Deep Residual Learning for Small-Footprint Keyword Spotting
castorini/honk
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We explore the application of deep residual learning and dilated convolutions to the keyword spotting task, using the recently-released Google Speech Commands Dataset as our benchmark.
Efficient keyword spotting using dilated convolutions and gating
snipsco/tract
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We explore the application of end-to-end stateless temporal modeling to small-footprint keyword spotting as opposed to recurrent networks that model long-term temporal dependencies using internal states.
Attention-based End-to-End Models for Small-Footprint Keyword Spotting
In this paper, we propose an attention-based end-to-end neural approach for small-footprint keyword spotting (KWS), which aims to simplify the pipelines of building a production-quality KWS system.
Small-Footprint Keyword Spotting on Raw Audio Data with Sinc-Convolutions
renyuanL/ry-Speech-commands
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Keyword Spotting (KWS) enables speech-based user interaction on smart devices.
Neural ODE with Temporal Convolution and Time Delay Neural Networks for Small-Footprint Keyword Spotting
fkhiro/kws-ode
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In this paper, we propose neural network models based on the neural ordinary differential equation (NODE) for small-footprint keyword spotting (KWS).
Small-Footprint Keyword Spotting with Multi-Scale Temporal Convolution
Based on the purposed model, we replace standard temporal convolution layers with MTConvs that can be trained for better performance.
The NPU System for the 2020 Personalized Voice Trigger Challenge
This paper describes the system developed by the NPU team for the 2020 personalized voice trigger challenge.
