In this paper, we present a new approach towards user-customized password speaker verification combining the advantages of hybrid HMM/ANN systems, usingArtificial Neural Networks (ANN) to estimate emission probabilities of Hidden Markov Models , and Gaussian Mixture Models. In the approach presented here, we indeed exploit the properties of hybrid HMM/ANN systems, usually resulting in high phon...

Mathematical models have been extensively used to shape living organism behaviour. These models are based on the N-dimensional space classification for those in which the patterns may have been defined. GeNeSys neural network family has been postulated as a global, comprehensive solution that shapes an individual behaviour. This article describes the GeNeSys family and presents some theoretical...

Although in theory, the speaker verification accuracy will not decrease with the number of users increasing, in fact, is not the case. In view of this situation, this paper expands the identity authentication scheme based on speaker recognition which is safe but deficient. After expansion, the identity authentication scheme based on speaker verification is more effective and practical.

Usually, people talk neutrally in environments where there are no abnormal talking conditions such as stress and emotion. Other emotional conditions that might affect people talking tone like happiness, anger, and sadness. Such emotions are directly affected by the patient health status. In neutral talking environments, speakers can be easily verified, however, in emotional talking environments...

Recent research shows that deep neural networks (DNNs) can be used to extract deep speaker vectors (d-vectors) that preserve speaker characteristics and can be used in speaker verification. This new method has been tested on text-dependent speaker verification tasks, and improvement was reported when combined with the conventional i-vector method. This paper extends the d-vector approach to sem...

A new background speaker modelling method is presented in this paper for text-independent speaker verification using Gaussian mixture models. This method does not require speech databases of other speakers to build background speaker models. A background model can be built directly from the same claimed speaker's database and has a smaller number of Gaussian mixtures compared to the claimed spe...

In this paper, a novel approach is presented for text-independent speaker verification. A quantized acoustic trajectory is constructed for each utterance based on Universal Background Model (UBM). Analysis of the speaker entropy in the trajectory space demonstrates that the segmental trajectory catches some speaker-specific information, which can be used to discriminate different speakers. Gaus...

For many years, i-vector based audio embedding techniques were the dominant approach for speaker verification and speaker diarization applications. However, mirroring the rise of deep learning in various domains, neural network based audio embeddings, also known as d-vectors, have consistently demonstrated superior speaker verification performance. In this paper, we build on the success of dvec...

The changes that occur in the human voice due to ageing have been well documented. The impact of these changes on speaker verification is less clear. In this work, we examine the effect of long-term vocal ageing on a speaker verification system. On a cohort of 13 adult speakers, using a conventional GMM-UBM system, we carry out longitudinal testing of each speaker across a time span of 30-40 ye...

Speech recognition systems usually need a feature extraction stage which aims at obtaining the best signal representation. State of the art speaker verification systems are based on cepstral features like MFCC, LFCC or LPCC. In this article, we propose a feature extraction system based on the combination of three feature extractors adapted to the speaker verification task. A genetic algorithm i...