标题： 一种适用于移动全双工语音交互的小型声学回声消除解决方案 显示英文标题

标题： A Small-footprint Acoustic Echo Cancellation Solution for Mobile Full-Duplex Speech Interactions

摘要： 在全双工语音交互系统中，有效的声学回声消除（AEC）对于恢复受回声污染的语音至关重要。本文提出了一种基于神经网络的AEC解决方案，以应对移动场景中的各种挑战，包括不同的硬件、非线性失真和长延迟。我们首先结合多种数据增强策略，以提高模型在不同环境中的鲁棒性。此外，采用渐进式学习来逐步提高AEC的效果，从而显著提升了语音质量。为了进一步优化AEC的下游应用，我们引入了一种新颖的后处理策略，采用专门为语音活动检测（VAD）和自动语音识别（ASR）等任务设计的定制参数，从而提高了它们的整体效果。最后，我们的方法采用了一个小尺寸模型和流式推理，使得在移动设备上的无缝部署成为可能。实证结果表明，所提出的方法在回声返回损耗增强和语音质量感知评估方面具有有效性，并且在VAD和ASR结果上均有显著提升。 显示英文摘要

摘要： In full-duplex speech interaction systems, effective Acoustic Echo Cancellation (AEC) is crucial for recovering echo-contaminated speech. This paper presents a neural network-based AEC solution to address challenges in mobile scenarios with varying hardware, nonlinear distortions and long latency. We first incorporate diverse data augmentation strategies to enhance the model's robustness across various environments. Moreover, progressive learning is employed to incrementally improve AEC effectiveness, resulting in a considerable improvement in speech quality. To further optimize AEC's downstream applications, we introduce a novel post-processing strategy employing tailored parameters designed specifically for tasks such as Voice Activity Detection (VAD) and Automatic Speech Recognition (ASR), thus enhancing their overall efficacy. Finally, our method employs a small-footprint model with streaming inference, enabling seamless deployment on mobile devices. Empirical results demonstrate effectiveness of the proposed method in Echo Return Loss Enhancement and Perceptual Evaluation of Speech Quality, alongside significant improvements in both VAD and ASR results.