Residual, Mixer, and Attention: The Three-way Combination for Streaming Wake Word Detection Framework

Speech interactions and digital assistants rely on effective wake word detection models to identify predefined words. In this study, we address the need for an efficient wake word detection model and propose a "three-way residual separable convolution network" (3W-ResSC) inspired by human multi-perspective learning. Our 3W-ResSC model combines three information: residual learning (ResNet), point-wise pattern, and depth-wise patterns from a CNN mixer (Mi). In addition, to conclude and emphasize information, our independent multi-view attention (iMVA) is proposed based attention mechanism (At). Based on ResNet, Mi, and At, we propose three different combinations for our model; ResNet+Mi, ResNet+At, and ResNet+Mi+At. By leveraging the strengths of these three combinations, our combinations are investigated to find the outstanding performance and pattern. To evaluate our model, we conduct experiments on multiple datasets, including Gowajee, HeyFireFox, HeySnips, and GSCv2. The results demonstrate that our 3W-ResSC outperforms baseline models in terms of equal error rate (EER), Matthews correlation coefficient (MCC), and false rejection rate (FRR) in multi-class classification. Additionally, we introduce a wake word detection framework specifically designed for streaming processing. This framework leverages an independent speech window, resembling a buffer-like streaming production process. Overall, our proposed 3W-ResSC model and wake word detection framework offer significant advancements in wake word detection, showcasing improved performance and efficiency, which discuss in various datasets and training sizes. These findings contribute to the development of more effective wake word detection and speech processing when using ResNet, Mi, and At in deep learning.