Implementation and Applications of WakeWords Integrated with Speaker Recognition: A Case Study

TL;DR

This paper addresses secure access in voice-activated embedded systems by combining wake-word activation with speaker recognition. The authors train wake-word detectors using real and synthetic data, leveraging a CNN-based model, and deploy Titanet for robust speaker verification with anti-spoofing. Empirical evaluation in real-world settings demonstrates feasibility and robustness, with synthetic data improving generalization across noise and speaker variability, though quantitative analysis is suggested for future work. The work highlights practical deployment considerations, including ROS and Docker integration, and paves the way for secure, user-friendly, voice-activated devices.

Abstract

This paper explores the application of artificial intelligence techniques in audio and voice processing, focusing on the integration of wake words and speaker recognition for secure access in embedded systems. With the growing prevalence of voice-activated devices such as Amazon Alexa, ensuring secure and user-specific interactions has become paramount. Our study aims to enhance the security framework of these systems by leveraging wake words for initial activation and speaker recognition to validate user permissions. By incorporating these AI-driven methodologies, we propose a robust solution that restricts system usage to authorized individuals, thereby mitigating unauthorized access risks. This research delves into the algorithms and technologies underpinning wake word detection and speaker recognition, evaluates their effectiveness in real-world applications, and discusses the potential for their implementation in various embedded systems, emphasizing security and user convenience. The findings underscore the feasibility and advantages of employing these AI techniques to create secure, user-friendly voice-activated systems.

Figures (1)

• Figure 1: NVIDIA TitaNet model architecture.