通过音素与语调协同学习，实现个性化关键词识别
ProKWS: Personalized Keyword Spotting via Collaborative Learning of Phonemes and Prosody
- 双流编码器分别学习音素和说话人特有语调特征
- 融合模块动态结合两者信息，在多种语境下表现稳定
- 适合语音助手、个性语音控制等需要定制化识别的场景
现有关键词检测系统主要依赖音素级匹配来区分易混淆词汇，但忽略了用户特有的发音特征，如语调（语调、重音、节奏）。本文提出ProKWS框架，将细粒度音素学习与个性化语调建模相结合。设计双流编码器：一路径通过对比学习提取鲁棒的音素表示，另一路径提取说话人特异的语调模式。一个协同融合模块动态整合音素与语调信息，提升在不同声学环境下的适应性。实验表明，ProKWS在标准基准上达到与最先进模型相当的性能，并对带有语调和意图变化的个性化关键词表现出强鲁棒性。
原文摘要 · Abstract (English)
Current keyword spotting systems primarily use phoneme-level matching to distinguish confusable words but ignore user-specific pronunciation traits like prosody (intonation, stress, rhythm). This paper presents ProKWS, a novel framework integrating fine-grained phoneme learning with personalized prosody modeling. We design a dual-stream encoder where one stream derives robust phonemic representations through contrastive learning, while the other extracts speaker-specific prosodic patterns. A collaborative fusion module dynamically combines phonemic and prosodic information, enhancing adaptability across acoustic environments. Experiments show ProKWS delivers highly competitive performance, comparable to state-of-the-art models on standard benchmarks and demonstrates strong robustness for personalized keywords with tone and intent variations.
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