Detecting Spoofed Noisy Speeches via Activation-Based Residual Blocks for Embedded Systems

Spoofed noisy speeches seriously threaten the speech-based embedded systems, such as smartphones and intelligent assistants. Consequently, we present an anti-spoofing detection model with activation-based residual blocks to identify spoofed noisy speeches with the requirements of high accuracy and low time overhead. Through theoretic analysis of noise propagation on shortcut connections of traditional residual blocks, we observe that different activation functions can help reducing the influence of noise under certain situations. Then, we propose a feature-aware activation function to weaken the influence of noise and enhance the anti-spoofing features on shortcut connections, in which a fine-grained processing is designed to remove noise and strengthen significant features. We also propose a variance-increasing-based optimization algorithm to find the optimal hyperparameters of the feature-aware activation function. Benchmark-based experiments demonstrate that the proposed method can reduce the average equal error rate of anti-spoofing detection from 21.72% to 4.51% and improve the accuracy by up to 37.06% and save up to 91.26% of time overhead on Jetson AGX Xavier compared with ten state-of-the-art methods.