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Voice Recognition Systems (VRSs) employ deep learning for speech recognition and speaker recognition. They have been widely deployed in various real-world applications, from intelligent voice assistance to telephony surveillance and biometric ...

Adversarial attacks have been posing significant security concerns to intelligent systems, such as speaker recognition systems (SRSs). Most attacks assume the neural networks in the systems are known beforehand, while black‐box attacks are ...

Many existing speaker recognition algorithms have the problem that single-domain feature extraction cannot represent the speech characteristics well, and this problem will affect the accuracy of speaker recognition. To solve this problem, we ...

Personal Voice Assistants (PVAs) are common acoustic sensing systems that are used as a speech-based controller for critical systems making them vulnerable to speech spoofing attacks. Prior research has focused on the discrimination of genuine and ...

Although deep neural networks have demonstrated state-of-the-art performance in several tasks such as speaker recognition among others, they are highly vulnerable to adversarial attacks. These attacks involve the transformation of the original ...

The integration of deep learning on Speaker Recognition (SR) advances its development and wide deployment, but also introduces the emerging threat of adversarial examples. However, only a few existing studies investigate its practical threat in physical ...

Automated speaker recognition uses data processing to identify speakers by their voice. Today, automated speaker recognition is deployed on billions of smart devices and in services such as call centres. Despite their wide-scale deployment and known ...

This paper presents a convolutional neural network with an attention mechanism for analysing the spectrogram in an x-vector based speaker recognition system. First, the convolutional neural network (CNN) is used to extract the features of the ...

Speaker recognition is proposed in this work using artificial neural network (ANN) and optimisation technique which finds wide variety of applications. Mel-frequency cepstral coefficient (MFCC) and linear prediction-filter coefficients (LPC) coefficients ...

Adversarial attacks against commercial black-box speech platforms, including cloud speech APIs and voice control devices, have received little attention until recent years. Constructing such attacks is difficult mainly due to the unique characteristics ...

Legislative proceedings present a rich source of multidimensional information that is crucial to citizens and journalists in a democratic system. At present, no fully automated solution exists that is capable of capturing all the necessary information ...

Unlike other biometric-based user identification methods (e.g., fingerprint and iris), speaker recognition systems can identify individuals relying on their unique voice biometrics without requiring users to be physically present. Therefore, speaker ...

Recently, there has been an increasing interest in end-to-end speaker recognition that directly take raw speech waveform as input without any hand-crafted features such as FBANK and MFCC. SincNet is a recently developed novel convolutional neural ...

Probabilistic linear discriminant analysis (PLDA) is a method used for biometric problems like speaker or face recognition that models the variability of the samples using two latent variables, one that depends on the class of the sample and another one ...

On a comprehensive speech database, speaker recognition characteristics are compared under the usage of various voice-source models. Inverse problems to find a source via vowel speech segments are solved on the base of a special speech-production model ...

In this paper, we focus on designing and developing ProMETheus, an intelligent system for meeting minutes generated from audio data. The first task in ProMETheus is to recognize the speakers from noisy audio data. Speaker recognition algorithm is used ...

This paper discusses energy-efficient acoustic feature-scoring using transistors with Gaussian-shaped Ids-Vgs. Acoustic feature-scoring is a critical step in speech recognition tasks such as speaker recognition. Suited to the transistor, we discuss a ...

We present a study using the Digital Democracy transcription tool. Human transcribers work to up-level and annotate California state legislative proceedings using the tool. Four phases of UI and functionality improvements are introduced and for each ...

In the recent past, deep learning methods have demonstrated remarkable success for supervised learning tasks in multiple domains including computer vision, natural language processing, and speech processing. In this article, we investigate the impact of ...

Authentication technologies based on biometrics, such as speaker recognition, are attracting more and more interest thanks to the elevated level of security offered by these technologies. Despite offering many advantages, such as remote use and low ...