TY - GEN
T1 - Speaker recognition based on lightweight neural network for smart home solutions
AU - Ai, Haojun
AU - Xia, Wuyang
AU - Zhang, Quanxin
N1 - Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019
Y1 - 2019
N2 - With the technological advancement of smart home devices, the lifestyles of people have been gradually changed. Meanwhile, speaker recognition is available in almost all smart home devices. Currently, the mainstream speaker recognition service is provided by a very deep neural network which trained on the cloud server. However, these deep neural networks are not suitable for deployment and operation on smart home devices. Aiming at this problem, in this paper, we propose a packet bottleneck method to improve SqueezeNet which has been widely used in the speaker recognition task. In the meantime, a lightweight structure named TrimNet has been designed. Besides, a model updating strategy based on transfer learning has been adopted to avoid model deteriorates due to the cold speech. The experimental results demonstrate that the proposed lightweight structure TrimNet is superior to SqueezeNet in classification accuracy, structural parameter quantity, and calculation amount. Moreover, the model updating method can increase the recognition rate of cold speech without damaging the recognition rate of other speakers.
AB - With the technological advancement of smart home devices, the lifestyles of people have been gradually changed. Meanwhile, speaker recognition is available in almost all smart home devices. Currently, the mainstream speaker recognition service is provided by a very deep neural network which trained on the cloud server. However, these deep neural networks are not suitable for deployment and operation on smart home devices. Aiming at this problem, in this paper, we propose a packet bottleneck method to improve SqueezeNet which has been widely used in the speaker recognition task. In the meantime, a lightweight structure named TrimNet has been designed. Besides, a model updating strategy based on transfer learning has been adopted to avoid model deteriorates due to the cold speech. The experimental results demonstrate that the proposed lightweight structure TrimNet is superior to SqueezeNet in classification accuracy, structural parameter quantity, and calculation amount. Moreover, the model updating method can increase the recognition rate of cold speech without damaging the recognition rate of other speakers.
KW - Smart home
KW - Speaker recognition
KW - Transfer learning
UR - http://www.scopus.com/inward/record.url?scp=85078518152&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-37352-8_37
DO - 10.1007/978-3-030-37352-8_37
M3 - Conference contribution
AN - SCOPUS:85078518152
SN - 9783030373511
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 421
EP - 431
BT - Cyberspace Safety and Security - 11th International Symposium, CSS 2019, Proceedings
A2 - Vaidya, Jaideep
A2 - Zhang, Xiao
A2 - Li, Jin
PB - Springer
T2 - 11th International Symposium on Cyberspace Safety and Security, CSS 2019
Y2 - 1 December 2019 through 3 December 2019
ER -