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English:Generative Adversarial Network Based i-Vector Transformation for Short Utterance Speaker Verification 
Japanese: ZHANG Jiacen, 井上 中順, 篠田 浩一.  
English: Jiacen Zhang, Nakamasa Inoue, Koichi Shinoda.  
Language English 
Journal/Book name
Japanese:2018年 秋季研究発表会 講演論文集 
English:ASJ 2018 Autumn Meeting 
Volume, Number, Page         pp. 1345-1346
Published date Aug. 29, 2018 
Japanese:一般社団法人 日本音響学会 
English:Acoustical Society of Japan 
Conference name
Japanese:日本音響学会 2018年 秋季研究発表会 
English:2018 Autumn Meeting of the Acoustical Society of Japan 
Conference site
Official URL http://www.asj.gr.jp/annualmeeting/index.html
Abstract i-Vector based text-independent speaker verification (SV) systems often have poor performance with short utterances, because the biased phonetic distribution in a short utterance makes the extracted i-vector unreliable. This paper proposes an i-vector compensation method using a generative adversarial network (GAN), where its generator network is trained to transform an unreliable i-vector from a short utterance into a reliable one which can only be extrated from a long utterance and its discriminator network is trained to determine whether an i-vector is from the generator or from a long utterance. Additionally, we assign two other learning tasks to the GAN to stabilize its training and to make the generated i-vector more speaker-specific. Speaker verification experiments conducted on the NIST SRE 2008 “short2-10sec” and “10sec-10sec” conditions show that our method can help reduce the average equal error rate of the conventional i-vector and PLDA system.

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