Text-Independent Speaker Verification Based on Triplet Loss

An improved end-to-end text-independent speaker verification model is proposed in this paper. LSTM networks are employed to extract the speaker model embedding, and the triplet loss is used to optimize the training of the network which make the training of the speaker verification model more efficient while keep the computation complexity relatively low. With the triplet loss, the proposed model can achieve better EER performance.