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Smart speakers, such as Amazon Echo, have been adopted by millions of users. However, the privacy impacts of smart speakers have not been well examined. We investigate the privacy leakage of smart speakers under an encrypted traffic analysis attack, referred to as voice command fingerprinting. In this attack, an adversary eavesdrops encrypted voice traffic from and to a smart speaker and infers which voice command a user says without decrypting encrypted traffic. We design our attacks based on neural networks and collect two large-scale datasets on Amazon Echo and Google Home by using an automatic traffic crawler.