wake-word-detection

The Wake Word Detection skill is designed to provide a solution for implementing always-listening, offline keyword spotting systems, specifically for detecting a wake word like 'Hey JARVIS'. By leveraging openWakeWord, this skill ensures efficient and effective wake word detection with minimal resource usage. The skill focuses on processing audio locally, preserving privacy, and minimizing unnecessary data transfer or storage. It enables voice assistants such as JARVIS to detect activation phrases while maintaining privacy, resource efficiency, and low latency. This skill solves the problem of creating a voice-activated system that doesn't depend on cloud services and can run efficiently on limited hardware or embedded devices.

Key features include continuous, low-latency audio monitoring, minimal CPU and memory usage, and a privacy-first approach that avoids sending sensitive data to external services. The system is designed to detect specific wake words with high accuracy, ensuring low false positives and quick responsiveness. The skill's core responsibilities include buffering only the audio needed for detection and clearing the buffer immediately after a wake word is recognized. Additionally, it allows users to easily pause or disable the system for added privacy. Its efficient design makes it suitable for use in devices that require long-running, always-on voice detection.

Use cases for this skill include voice-activated assistants like JARVIS, smart home devices, and offline voice command systems. Target users include developers building voice-activated systems, engineers focused on privacy, and teams working on resource-constrained devices where CPU and memory usage are critical considerations. This skill is also ideal for developers who want to build always-listening systems that prioritize privacy and efficiency, without relying on cloud-based services.