User-defined keyword spotting (KWS) enables personalized voice interaction by detecting user-specified keywords. A key challenge in this task is distinguishing target keywords from phonetically confusable alternatives. To address this challenge, we propose KFC-KWS, a multimodal framework that leverages connectionist temporal classification (CTC)-guided keyframe selection. Specifically, we exploit the peaky posterior distributions of CTC to identify high-confidence phoneme frames, enabling precise alignment across audio, phoneme, and text modalities. These keyframes are then fused with full-utterance representations through cross-attention to capture both local discriminative cues and global contextual information. On LibriPhrase, KFC-KWS achieves the best-balanced performance (98.73% AUC) and substantially outperforms advanced baselines on the challenging hard subset (97.65% AUC and 7.75% EER), demonstrating its effectiveness in discriminating between highly confusable keywords.

翻译：用户定义关键词唤醒（KWS）通过检测用户指定的关键词，实现个性化语音交互。该任务的一个核心挑战在于区分目标关键词与发音相似的混淆候选项。针对这一问题，我们提出KFC-KWS——一种利用连接主义时序分类（CTC）引导关键帧选择的多模态框架。具体而言，我们利用CTC峰值后验分布识别高置信度音素帧，从而实现音频、音素与文本模态间的精准对齐。随后，这些关键帧通过交叉注意力机制与完整语句表征进行融合，以同时捕获局部判别性线索与全局上下文信息。在LibriPhrase数据集上，KFC-KWS取得了最佳均衡性能（AUC达98.73%），并在具有挑战性的困难子集中大幅超越先进基线（AUC达97.65%，等错误率仅7.75%），证明了其在区分高度混淆关键词方面的有效性。