3 accepted papers
End-to-end systems using deep neural networks have been widely studied in the field of speaker verification. Raw audio signal processing has also been widely studied in the fields of automatic music tagging and speech recognition. However, as far as we know, end-to-end systems using raw audio signal…
We propose a method to improve speaker verification performance when a test utterance is very short. In some situations with short test utterances, performance of ivector/probabilistic linear discriminant analysis systems degrades. The proposed method transforms short-utterance feature vectors to ad…
Few studies on speaker verification have directly used a deep neural network (DNN) as a classifier. It is difficult to directly apply a DNN as a discriminative model to speaker-verification tasks because the training data for each speaker are very limited. Therefore, a b-vector has been proposed to…