Fast Enrollable Streaming Keyword Spotting System: Training and Inference using a Web Browser

When a keyword spotting system is deployed on heavily personalized platforms such as digital humans, a few issues occur such as 1) a lack of training data when registering user-defined keywords, 2) a desire to reduce computation and minimize latency, and 3) the inability to immediately train and test the keyword-spotting model. We address the issues through 1) a keyword-spotting system based on a speech embedding model, 2) streamable system with duplicate computations removed, and 3) real-time inference in a web browser using WebAssembly.