  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. Additionally, we assign two other learning
tasks to the GAN to stabilize its training and to make the generated ivector
more speaker-specific. Speaker verification experiments on the NIST SRE 2008
"10sec-10sec" condition show that our method reduced the equal error rate by
11.3% from the conventional i-vector and PLDA system.
