Keyword spotting techniques to improve the recognition accuracy of user-defined keywords

The existing keyword spotting (KWS) techniques can recognize pre-defined keywords well but have a poor recognition accuracy for user-defined keywords. In real use cases, there is a high demand for users to define their keywords for various reasons. To address the problem, in this work, three techniques have been proposed, including incremental training with revised loss function, data augmentation, and fine-grained training, to improve the accuracy for the user-defined keywords while maintaining high accuracy for pre-defined keywords. The proposed techniques are applied to a classical KWS model (cnn-trad-fpool3) and a state-of-the-art KWS model (res15) respectively. The experimental results show that the proposed techniques have better recognition accuracy than several existing methods for the recognition of use-defined keywords. With the proposed techniques, the recognition accuracy of user-defined keywords on cnn-trad-fpool3 and res15 are significantly improved by 21.78% and 24.42%, respectively.