Gsdnet: Gated Self-Supervised Denoising Speech Control Network

Abstract

The cost of labeling data remains high, even with the effective implementation of deep neural networks in speech recognition. At the same time, noise still hampers the performance of speech-recognition methods. Thus, it is still challenging to make full use of data sets to enhance the robustness of recognition systems. In this letter, we construct GSDNet, a gated self-supervised denoising speech control network that consists of three parts (a denoising feature-extraction frontend, a speech recognition encoder, and a decoder based on gated convolutionary neural networks with self-supervised regression), to provide a low-cost method for training a robust speech recognition system, and we apply it to equipment-control tasks. Finally, the experimental results with the THCH30 and AISHELL data sets for equipment control show that the word error rate is less than 0.2 without a language model.