轻量级多任务模型实现个性化关键词唤醒，兼顾隐私与效率
PCOV-KWS: Multi-task Learning for Personalized Customizable Open Vocabulary Keyword Spotting
- 用轻量网络同时做关键词识别和说话人验证
- 新损失函数避免类别间竞争，提升识别准确率
- 参数少、算力低，适合嵌入式设备部署
随着物联网（IoT）、自动语音识别（ASR）、说话人验证（SV）和文本转语音（TTS）等技术的发展，智能语音助手的使用日益普及，对隐私性和个性化的需求也显著上升。本文提出一种用于个性化可定制开放词汇关键词唤醒（PCOV-KWS）的多任务学习框架。该框架采用轻量级网络，同时执行关键词唤醒（KWS）与说话人验证（SV），以满足个性化唤醒需求。引入不同于Softmax的训练准则，将多分类问题转化为多个二分类问题，消除了类别间的竞争关系；同时在训练中采用多任务损失加权优化策略。我们在多个数据集上评估了PCOV-KWS系统，结果表明其优于基线方法，且所需参数更少、计算资源更低。
原文摘要 · Abstract (English)
As advancements in technologies like Internet of Things (IoT), Automatic Speech Recognition (ASR), Speaker Verification (SV), and Text-to-Speech (TTS) lead to increased usage of intelligent voice assistants, the demand for privacy and personalization has escalated. In this paper, we introduce a multi-task learning framework for personalized, customizable open-vocabulary Keyword Spotting (PCOV-KWS). This framework employs a lightweight network to simultaneously perform Keyword Spotting (KWS) and SV to address personalized KWS requirements. We have integrated a training criterion distinct from softmax-based loss, transforming multi-class classification into multiple binary classifications, which eliminates inter-category competition, while an optimization strategy for multi-task loss weighting is employed during training. We evaluated our PCOV-KWS system in multiple datasets, demonstrating that it outperforms the baselines in evaluation results, while also requiring fewer parameters and lower computational resources.
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