6 accepted papers
Malicious users attempt to replicate commercial models functionally at low cost by training a clone model with query responses. It is challenging to timely prevent such model-stealing attacks to achieve strong protection and maintain utility. In this paper, we propose a novel non-parametric detector…
In conventional deep speaker embedding frameworks, the pooling layer aggregates all frame-level features over time and computes their mean and standard deviation statistics as inputs to subsequent segment-level layers. Such statistics pooling strategy produces fixed-length representations from varia…
Speech emotion recognition is crucial to human-computer interaction. The temporal regions that represent different emotions scatter in different parts of the speech locally. Moreover, the temporal scales of important information may vary over a large range within and across speech segments. Although…
State-of-the-art speaker recognition systems are trained with a large amount of human-labeled training data set. Such a training set is usually composed of various data sources to enhance the modeling capability of models. However, in practical deployment, unseen condition is almost inevitable. Doma…
In this study, we present the VoiceAI (VAI) submissions to NIST SRE 2019 challenge on the task of speaker recognition using conversational telephone speech. Domain mismatching remains a challenging problem on SRE19. However, participants are unconstrained to use any public or proprietary data to mit…
In order to enhance the security of automatic speaker verification (ASV) systems, automatic spoofing attack detection, which discriminates the fake audio recordings from genuine human speech, has gain much attention recently. Among various ways of spoofing attacks, replay attacks are one of the most…