Abstract: Current forensic voice identification is typically conducted under ideal conditions; however, speech signal distortion caused by varying channel conditions significantly increases the complexity of identification. To help students deeply understand and address this challenge, this teaching design focuses on exploring the impact of different channels on voice identification. In the experimental instruction, students record and analyze voice samples from multiple channels, including mobile calls, social communication apps, law enforcement recorders, and voice recorders. Using intelligent audio analysis software, students compare spectrogram feature differences across channels and learn to evaluate how these mismatches affect voice identification. The experiment integrates hands-on operations, technical principle explanations, and discussions on improving existing identification procedures. Through this comprehensive experiment, students gain a holistic understanding of the technical challenges and developmental trends in voice identification, thereby enhancing their practical and innovative thinking. Furthermore, the experiment introduces discussions on cutting-edge technologies such as channel compensation, pre-emphasis, and speech enhancement, broadening students’ awareness of advancements in forensic voice identification technology.