多通道注意力机制提升嘈杂环境下的关键词识别准确率
Multichannel Keyword Spotting for Noisy Conditions
- 采用多通道输入与注意力机制自动选择有效信号
- 在实验室和真实智能设备数据上均显著提升识别效果
- 兼顾降噪性能与计算效率，适合边缘设备部署
本文提出一种改进的关键词检测（KWS）算法，以应对噪声环境。传统波束成形（BF）和自适应噪声消除（ANC）虽在某些条件下有效，但可能扭曲或抑制有用信号，影响激活系统性能。作者设计了一种神经网络架构，通过多个输入通道与注意力机制，动态选择最有效的通道或组合。实验在两个数据集上验证：一是受控实验室环境，二是真实智能音箱采集的自然场景数据。与多种基线方法对比，该算法在降噪指标、KWS性能及计算资源消耗方面均表现更优。
原文摘要 · Abstract (English)
This article presents a method for improving a keyword spotter (KWS) algorithm in noisy environments. Although beamforming (BF) and adaptive noise cancellation (ANC) techniques are robust in some conditions, they may degrade the performance of the activation system by distorting or suppressing useful signals. The authors propose a neural network architecture that uses several input channels and an attention mechanism that allows the network to determine the most useful channel or their combination. The improved quality of the algorithm was demonstrated on two datasets: from a laboratory with controlled conditions and from smart speakers in natural conditions. The proposed algorithm was compared against several baselines in terms of the quality of noise reduction metrics, KWS metrics, and computing resources in comparison with existing solutions.
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