  This paper presents an end-to-end text-independent speaker verification
framework by jointly considering the speaker embedding (SE) network and
automatic speech recognition (ASR) network. The SE network learns to output an
embedding vector which distinguishes the speaker characteristics of the input
utterance, while the ASR network learns to recognize the phonetic context of
the input. In training our speaker verification framework, we consider both the
triplet loss minimization and adversarial gradient of the ASR network to obtain
more discriminative and text-independent speaker embedding vectors. With the
triplet loss, the distances between the embedding vectors of the same speaker
are minimized while those of different speakers are maximized. Also, with the
adversarial gradient of the ASR network, the text-dependency of the speaker
embedding vector can be reduced. In the experiments, we evaluated our speaker
verification framework using the LibriSpeech and CHiME 2013 dataset, and the
evaluation results show that our speaker verification framework shows lower
equal error rate and better text-independency compared to the other approaches.
