Small-Footprint Keyword Spotting
7 papers with code • 0 benchmarks • 0 datasets
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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.
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.
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.
Neural ODE with Temporal Convolution and Time Delay Neural Networks for Small-Footprint Keyword Spotting
In this paper, we propose neural network models based on the neural ordinary differential equation (NODE) for small-footprint keyword spotting (KWS).
Based on the purposed model, we replace standard temporal convolution layers with MTConvs that can be trained for better performance.