用示例查询实现自定义关键词识别，提升效率与准确性。
Query-by-Example Keyword Spotting Using Spectral-Temporal Graph Attentive Pooling and Multi-Task Learning
- 构建谱时图注意力池化与多任务学习框架，提取鲁棒特征。
- 轻量级LiCoNet模型在629人数据集上达1.98%误报率，性能接近复杂模型。
- 适合需要低延迟、高定制化的智能设备语音交互场景。
现有关键词检测（KWS）系统主要依赖预定义关键词短语，但自定义关键词识别对个性化智能设备交互至关重要。本文提出一种新型查询示例（QbyE）KWS系统，采用谱时图注意力池化与多任务学习框架，旨在有效学习说话人无关且富含语言信息的嵌入表征。研究对比了三种编码器结构：LiCoNet、Conformer和ECAPA_TDNN。在包含629名说话者的内部大规模数据集上，实验结果表明该框架能充分发挥轻量模型潜力。尤其，效率高出13倍的LiCoNet在0.3次误报/小时条件下实现1.98%的漏报率，性能接近计算开销更大的Conformer模型（1.63%）。
原文摘要 · Abstract (English)
Existing keyword spotting (KWS) systems primarily rely on predefined keyword phrases. However, the ability to recognize customized keywords is crucial for tailoring interactions with intelligent devices. In this paper, we present a novel Query-by-Example (QbyE) KWS system that employs spectral-temporal graph attentive pooling and multi-task learning. This framework aims to effectively learn speaker-invariant and linguistic-informative embeddings for QbyE KWS tasks. Within this framework, we investigate three distinct network architectures for encoder modeling: LiCoNet, Conformer and ECAPA_TDNN. The experimental results on a substantial internal dataset of $629$ speakers have demonstrated the effectiveness of the proposed QbyE framework in maximizing the potential of simpler models such as LiCoNet. Particularly, LiCoNet, which is 13x more efficient, achieves comparable performance to the computationally intensive Conformer model (1.98% vs. 1.63\% FRR at 0.3 FAs/Hr).
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