Effectiveness in the Realisation of Speaker Authentication

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.An important consideration for the deployment of speaker recognition in authentication applications is the approach to the formation of training and testing utterances . Whilst defining this for a specific scenario is influenced by the associated requirements and conditions, the process can be further guided through the establishment of the relative usefulness of alternative frameworks for composing the training and testing material. In this regard, the present paper provides an analysis of the effects, on the speaker recognition accuracy, of various bases for the formation of the training and testing data. The experimental investigations are conducted based on the use of digit utterances taken from the XM2VTS database. The paper presents a detailed description of the individual approaches considered and discusses the experimental results obtained in different cases

Synthetic speech detection using phase information

Taking advantage of the fact that most of the speech processing techniques neglect the phase information, we seek to detect phase perturbations in order to prevent synthetic impostors attacking Speaker Verification systems. Two Synthetic Speech Detection (SSD) systems that use spectral phase related information are reviewed and evaluated in this work: one based on the Modified Group Delay (MGD), and the other based on the Relative Phase Shift, (RPS). A classical module-based MFCC system is also used as baseline. Different training strategies are proposed and evaluated using both real spoofing samples and copy-synthesized signals from the natural ones, aiming to alleviate the issue of getting real data to train the systems. The recently published ASVSpoof2015 database is used for training and evaluation. Performance with completely unrelated data is also checked using synthetic speech from the Blizzard Challenge as evaluation material. The results prove that phase information can be successfully used for the SSD task even with unknown attacks.This work has been partially supported by the Basque Government (ElkarOla Project, KK-2015/00,098) and the Spanish Ministry of Economy and Competitiveness (Restore project, TEC2015-67,163-C2-1-R)

The SJTU System for Short-duration Speaker Verification Challenge 2021

This paper presents the SJTU system for both text-dependent and text-independent tasks in short-duration speaker verification (SdSV) challenge 2021. In this challenge, we explored different strong embedding extractors to extract robust speaker embedding. For text-independent task, language-dependent adaptive snorm is explored to improve the system performance under the cross-lingual verification condition. For text-dependent task, we mainly focus on the in-domain fine-tuning strategies based on the model pre-trained on large-scale out-of-domain data. In order to improve the distinction between different speakers uttering the same phrase, we proposed several novel phrase-aware fine-tuning strategies and phrase-aware neural PLDA. With such strategies, the system performance is further improved. Finally, we fused the scores of different systems, and our fusion systems achieved 0.0473 in Task1 (rank 3) and 0.0581 in Task2 (rank 8) on the primary evaluation metric.Comment: Published by Interspeech 202