3260 papers • 126 benchmarks • 313 datasets
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This work explores the application of deep residual learning and dilated convolutions to the keyword spotting task, using the recently-released Google Speech Commands Dataset as a benchmark and establishes an open-source state-of-the-art reference to support the development of future speech-based interfaces.
A model inspired by the recent success of dilated convolutions in sequence modeling applications, allowing to train deeper architectures in resource-constrained configurations, and applies a custom target labeling that back-propagates loss from specific frames of interest, therefore yielding higher accuracy and only requiring to detect the end of the keyword.
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, which outperforms the recent Deep KWS approach by a large margin and the best performance is achieved by CRNN.
This paper presents techniques to apply NODE to KWS that make it possible to adopt Batch Normalization to NODE-based network and to reduce the number of computations during inference.
The end-to-end architecture extracts spectral features using parametrized Sinc-convolutions and achieves the competitive accuracy of 96.4% on Google’s Speech Commands test set with only 62k parameters.
This paper proposes a multi-branch temporal convolution module (MTConv), a CNN block consisting of multiple temporal Convolution filters with different kernel sizes, which enriches temporal feature space, and proposes a temporal efficient neural network (TENet) designed for KWS system.
This paper describes the system developed by the NPU team for the 2020 personalized voice trigger challenge, consisting of two independently trained subsystems: a small footprint keyword spotting (KWS) system and a speaker verification (SV) system.
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