End-to-end DNN based text-independent speaker recognition for long and short utterances. (January 2020)

• Record Type: Journal Article
• Authors: Rohdin, Johan; Silnova, Anna; Diez, Mireia; Plchot, Oldřich; Matějka, Pavel; Burget, Lukáš; Glembek, Ondřej
• Is Part Of: Computer speech & language. Volume 59(2020)
• Page Start: 22
• Page End: 35
• Publication Date: 2020-01
• Subjects: Speaker verification -- DNN -- End-to-end -- Text-independent -- i-vector -- PLDA; Speech processing systems -- Periodicals; Automatic speech recognition -- Periodicals; Computers -- Periodicals; Linguistics -- Periodicals; Speech-Language Pathology -- Periodicals; Traitement automatique de la parole -- Périodiques; Reconnaissance automatique de la parole -- Périodiques; Automatic speech recognition; Speech processing systems; Electronic journals; Periodicals; 006.454
• DOI: 10.1016/j.csl.2019.06.002
• Languages: English
• ISSNs: 0885-2308

Abstract

Abstract: Recently several end-to-end speaker verification systems based on deep neural networks (DNNs) have been proposed. These systems have been proven to be competitive for text-dependent tasks as well as for text-independent tasks with short utterances. However, for text-independent tasks with longer utterances, end-to-end systems are still outperformed by standard i-vector + PLDA systems. In this work, we present an end-to-end speaker verification system that is initialized to mimic an i-vector + PLDA baseline. The system is then further trained in an end-to-end manner but regularized so that it does not deviate too far from the initial system. In this way we mitigate overfitting which normally limits the performance of end-to-end systems. The proposed system outperforms the i-vector + PLDA baseline on both long and short duration utterances.