Jacobian Adaptation (JA) has been successfully used in Automatic Speech Recognition (ASR) systems to adapt the acoustic models from the training to the testing noise conditions. In this work we present an improvement of JA for speaker verification, where a specific training noise reference is estimated for each speaker model. The new proposal, which will be referred to as Model-dependent Noise ...

This paper presents a multi-biometric verification system that combines speaker verification, fingerprint verification with face identification. Their respective equal error rates (EER) are 4.3%, 5.1% and the range of (5.1% to 11.5%) for matched conditions in facial image capture. Fusion of the three by majority voting gave a relative improvement of 48% over speaker verification (i.e. the best-...

Text-dependent speaker verification uses short utterances and verifies both speaker identity and text contents. Due to this nature, traditional state-of-the-art speaker verification approaches, such as i-vector, may not work well. Recently, there has been interest of applying deep learning to speaker verification, however in previous works, standalone deep learning systems have not achieved sta...

In this paper we provide a brief overview of the area of speaker recognition, defining terminology, discussing applications, describing underlying techniques and providing some indications of performance. Following this overview we compare speaker verification to other biometrics and discuss some of the strengths and weaknesses of speaker verification technology. Finally we outline some potenti...

This paper presents an overview of a state-of-the-art text-independent speaker verification system using score normalization. First, an introduction proposes a modular scheme of the training and test phases of a speaker verification system. Then, the most commonly speech parameterization used in speaker verification, namely, cepstral analysis, is detailed. Normalization of scores is then explai...

We propose discrimination method of synthetic speech using pitch pattern of speech signal. By applying the proposed synthetic speech discrimination system as pre-process before the conventional HMM speaker verification system, we can improve the safety of conventional speaker verification system against imposture using synthetic speech. The proposed method distinguishes between synthetic speech...

Speaker verification performance is adversely affected by mismatches between training and testing data in intrinsic variations. This paper explores how recent technologies focused on modeling the total variability behave in addressing the effects of intrinsic variation in speaker verification. The effects of intrinsic variation are investigated from six aspects including speaking style, speakin...

This chapter overviews techniques for evaluating speech and speaker recognition systems. The chapter first describes principles of recognition methods, and specifies types of systems as well as their applications. The evaluation methods can be classified into subjective and objective methods, among which the chapter focuses on the latter methods. In order to compare/normalize performances of di...