Improvement of the learning process of the automated speaker recognition system for critical use with HMM-DNN component

Bykov, Mykola M.; Kovtun, Viacheslav; Kobylyanska, Iryna M.; Wójcik, Waldemar; Smailova, Saule

doi:10.1117/12.2536888

Cited by 4 publications

(2 citation statements)

References 18 publications

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“…The processing of the latter was studied by the authors in [26,27]. Because of this, a significant number of studies have been conducted on the use of deep networks in compression tasks, namely image and video compression tasks [28].…”

Section: Machine Learning Algorithms For Video Compressionmentioning

confidence: 99%

A Comparison of Machine Learning-Based and Conventional Technologies for Video Compression

Mochurad

2024

Technologies

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The growing demand for high-quality video transmission over bandwidth-constrained networks and the increasing availability of video content have led to the need for efficient storage and distribution of large video files. To improve the latter, this article offers a comparison of six video compression methods without loss of quality. Particularly, H.255, VP9, AV1, convolutional neural network (CNN), recurrent neural network (RNN), and deep autoencoder (DAE). The proposed decision is to use a dataset of high-quality videos to implement and compare the performance of classical compression algorithms and algorithms based on machine learning. Evaluations of the compression efficiency and the quality of the received images were made on the basis of two metrics: PSNR and SSIM. This comparison revealed the strengths and weaknesses of each approach and provided insights into how machine learning algorithms can be optimized in future research. In general, it contributed to the development of more efficient and effective video compression algorithms that can be useful for a wide range of applications.

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Section: Machine Learning Algorithms For Video Compressionmentioning

confidence: 99%

A Comparison of Machine Learning-Based and Conventional Technologies for Video Compression

Mochurad

2024

Technologies

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“…Using a high number of Gaussian distributions per voice model [13][14][15]. In recent years, there has also been a trend towards the use of Deep Neural Networks (DNN) [17,18,24], including Convolutional Neural Networks (CNN) [19], as well as so-called Long Short-Term Memory Networks (LSTM) [16,20,21]. Another type of neural network is the Time Delay Neural Network (TDNN) [21][22][23][24][25], as well as the Sequence-to-Sequence Attentional Siamese Neural Network (Seq2Seq-ASNN) [26].…”

Section: Related Workmentioning

confidence: 99%

Enhancing Web Application Security: Advanced Biometric Voice Verification for Two-Factor Authentication

Kamiński,

Dobrowolski,

Piotrowski

et al. 2023

Electronics

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This paper presents a voice biometrics system implemented in a web application as part of a two-factor authentication (2FA) user login. The web-based application, via a client interface, runs registration, preprocessing, feature extraction and normalization, classification, and speaker verification procedures based on a modified Gaussian mixture model (GMM) algorithm adapted to the application requirements. The article describes in detail the internal modules of this ASR (Automatic Speaker Recognition) system. A comparison of the performance of competing ASR systems using the commercial NIST 2002 SRE voice dataset tested under the same conditions is also presented. In addition, it presents the results of the influence of the application of cepstral mean and variance normalization over a sliding window (WCMVN) and its relevance, especially for voice recordings recorded in varying acoustic tracks. The article also presents the results of the selection of a reference model representing an alternative hypothesis in the decision-making system, which significantly translates into an increase in the effectiveness of speaker verification. The final experiment presented is a test of the performance achieved in a varying acoustic environment during remote voice login to a web portal by the test group, as well as a final adjustment of the decision-making threshold.

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Prediction of Magnetic Remanence of Sm-Co Magnets Using Machine Learning Algorithms

Trostianchyn

Izonin

Tkachenko

et al. 2022

Lecture Notes in Networks and Systems

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Improvement of the learning process of the automated speaker recognition system for critical use with HMM-DNN component

Cited by 4 publications

References 18 publications

A Comparison of Machine Learning-Based and Conventional Technologies for Video Compression

A Comparison of Machine Learning-Based and Conventional Technologies for Video Compression

Enhancing Web Application Security: Advanced Biometric Voice Verification for Two-Factor Authentication

Prediction of Magnetic Remanence of Sm-Co Magnets Using Machine Learning Algorithms

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