arXiv AI By Gaopeng Xu, Chengfei Li, Xianliang Wang, Lin Zhu, Juan Wei, Wenpeng Li, Jianwei Niu, Jie Gao

PromptKWS: A Novel Prompt-Guided Open-Vocabulary Keyword Spotting Framework

Read the original on arXiv AI →

PromptKWS is a new prompt‑guided keyword spotting framework that enhances open‑vocabulary KWS accuracy. It introduces the Prompt Phrases Prediction Network (PPN), an encoder‑decoder that extracts keyword prompt embeddings, which are then integrated into the KWS encoder via a Prompt‑acoustic Multi‑head Cross‑attention module. Experiments show that PromptKWS raises the wake‑up rate by more than 10% and improves overall accuracy by over 15% in noisy, pronunciation‑variant real‑world settings.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI
Sep 24

Training Intelligent Voice Assistant Wakeup with Controllable Synthetic Conversations

The paper presents a new wake‑up system for voice assistants that goes beyond simple keyword spotting by adding contextual trigger detection. After the wake word is heard, the system reasons to differentiate between actual user commands and unrelated speech, enabling more efficient and context‑aware interactions. A data‑generation architecture is introduced that creates a 62.3‑hour corpus of controllable multi‑speaker conversations, including direct invocations, contextual follow‑ups, and non‑addressed speech, and experimental results confirm the approach’s effectiveness across varied synthetic scenarios.

By Marcin Sowa\'nski, Kacper Leszczy\'nski, Kacper Krzywicki, Krzysztof Wodnicki