AI 中文总结
该研究提出PromptKWS框架，通过PPN和MHCA提升开放词汇KWS系统性能，在噪声等复杂环境中唤醒率超基线10%、平均准确率超纯声学模型15%
AI 中文摘要
本文提出了 PromptKWS，一种新型的提示引导式关键词 spotting（KWS）框架，用于提升开放词汇 KWS 系统的准确率。具体而言，我们引入了提示短语预测网络（PPN），这是一种编码器-解码器架构，旨在有效提取关键词提示嵌入。我们利用 PPN 编码器对关键词提示进行编码，并通过提示-声学多头交叉注意力（MHCA）将提示嵌入注入到提示引导式 KWS 编码器中。实验表明，与基线系统相比，PromptKWS 将唤醒率提升了 10%以上。值得注意的是，PromptKWS 的另一优势在于其能够有效利用关键词提示，以适应包含噪声和发音变异的复杂真实世界环境。与在这类场景中常遇困难的纯声学模型相比，PromptKWS 表现出卓越性能，在测试集上的平均准确率提升超过 15%。
英文摘要
In this paper, we present PromptKWS, a novel Prompt-guided keyword spotting (KWS) framework to improve the accuracy of open vocabulary KWS systems. In specific terms, we introduce the Prompt Phrases Prediction Network (PPN), an encoder-decoder architecture designed to effectively extract keyword prompts embeddings. we employ the PPN encoder to encode the keyword prompts and infuse the prompt embedding into the Prompt-guided KWS encoder by utilizing a Prompt-acoustic Multi-head Cross-attention (MHCA). Experiments show that PromptKWS improves the wakeup rate by over 10% compared to baseline system. Notably, another strength of PromptKWS is its ability to effectively leverage keyword prompts for adapting to complex real-world environments involving noise and pronunciation variations. In comparison to purely acoustic models, which often struggle in such situations, PromptKWS demonstrates remarkable performance, with an average accuracy improvement of over 15% in test sets.
