Speaker Recognition Using MobileNetV3 for Voice-Based Robot Navigation

Abstract

Several studies have implemented speaker recog-nition as part of Human-Robot Interaction (HRI) to enhance the perception capabilities of voice-based robots. In this context, our research is implemented on a delivery robot that requires constraints on control and interaction. Implementing a speaker recognition system for a delivery robot is designed to ensure that the robot only executes commands from authorized speakers while avoiding receiving commands from unauthorized speakers. We propose text-independent speaker identification based on speaker embedding d-vector with MobileNetV3, then compared with a Fast ResNet-34. In addition, the compatibility of different feature extraction representations, MFCC, and Mel-scaled spec-trogram is evaluated for the proposed architecture. The proposed system has been evaluated on a dataset in Bahasa Indonesia with various acoustic environments. The results of the proposed approach have a better computing efficiency of 98.27 %, a smaller model size with an 87.47 % reduction, and a faster inference time of about 7ms compared to Fast ResNet-34.