Abstract: Current voiceprint identity verification is typically conducted under ideal conditions; however, speech signal distortion caused by varying channel conditions significantly increases the complexity of verification. To help students deeply understand and address this challenge, this teaching design focuses on exploring the impact of different channels on voiceprint identification. In the experimental instruction, students record and analyze voice samples from multiple channels, including mobile calls, network communication software, law enforcement recorders, and voice recorders. Using intelligent audio analysis software, students compare spectrogram feature differences across channels and learn to evaluate how these variations affect voiceprint recognition. The experiment integrates hands-on operations, technical principle explanations, and discussions on improving existing verification workflows. Through this comprehensive experiment, students gain a holistic understanding of the technical challenges and developmental trends in voiceprint recognition, 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 voiceprint verification technology.