I-vector Transformation Using Conditional Generative Adversarial Networks for Short Utterance Speaker Verification

1 Apr 2018Jiacen ZhangNakamasa InoueKoichi Shinoda

I-vector based text-independent speaker verification (SV) systems often have poor performance with short utterances, as 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 generate a compensated i-vector from a short-utterance i-vector and its discriminator network is trained to determine whether an i-vector is generated by the generator or the one extracted from a long utterance... (read more)

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