<?xml version="1.0" encoding="UTF-8"?>
<doi_batch version="4.3.0" xmlns="http://www.crossref.org/doi_resources_schema/4.3.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.crossref.org/doi_resources_schema/4.3.0 http://www.crossref.org/schema/deposit/doi_resources4.3.0.xsd">
<head>
<doi_batch_id>6c13e3e0-8ef4-4441-af80-74fa193775f0</doi_batch_id>
<depositor>
<name>beie</name>
<email_address>director@blueeyesintelligence.org</email_address>
</depositor>
</head>
<body>
<doi_citations>
<doi>10.35940/ijrte.C7862.0912323</doi>
<citation_list><citation key="ref0"><doi>10.3390/make1010031</doi><unstructured_citation>P. Dhakal, P. Damacharla, A. Y. Javaid, and V. Devabhaktuni, &quot;A Near Real-Time Automatic Speaker Recognition Architecture for Voice-Based User Interface,&quot; Mach. Learn. Knowl. Extr., vol. 1, no. 1, pp. 504-520, 2019, doi: 10.3390/make1010031. [CrossRef]</unstructured_citation></citation><citation key="ref1"><doi>10.1109/INDICON49873.2020.9342423</doi><unstructured_citation>A. V. Amrutha, K. H. Anagha, A. Kamal K, and B. Kumaraswamy, &quot;Multi-level Speaker Authentication: An Overview and Implementation,&quot; in 2020 IEEE 17th India Council International Conference, INDICON 2020, 2020. doi: 10.1109/INDICON49873.2020.9342423. [CrossRef]</unstructured_citation></citation><citation key="ref2"><doi>10.1145/3433210.3437518</doi><unstructured_citation>S. Abhishek Anand, J. Liu, C. Wang, M. Shirvanian, N. Saxena, and Y. Chen, &quot;EchoVib: Exploring Voice Authentication via Unique Non-Linear Vibrations of Short Replayed Speech,&quot; in ASIA CCS 2021 - Proceedings of the 2021 ACM Asia Conference on Computer and Communications Security, 2021. doi: 10.1145/3433210.3437518. [CrossRef]</unstructured_citation></citation><citation key="ref3"><unstructured_citation>B. Chettri, &quot;Voice Biometric System Security: Design and Analysis of Countermeasures for Replay Attacks,&quot; 2020. [CrossRef]</unstructured_citation></citation><citation key="ref4"><doi>10.1109/BCI51272.2021.9385338</doi><unstructured_citation>N. Kobayashi and T. Morooka, &quot;Application of High-accuracy Silent Speech BCI to Biometrics using Deep Learning,&quot; in 9th IEEE International Winter Conference on Brain-Computer Interface, BCI 2021, 2021. doi: 10.1109/BCI51272.2021.9385338. [CrossRef]</unstructured_citation></citation><citation key="ref5"><unstructured_citation>S. Kinkiri, W. J. C. Melis, and S. Keates, &quot;Machine learning for voice recognition,&quot; Second Medw. Eng. Conf. Syst. Effic. Sustain. Model., 2017.</unstructured_citation></citation><citation key="ref6"><doi>10.1109/BIOSIG52210.2021.9548296</doi><unstructured_citation>L. Chowdhury, M. Kamal, N. Hasan, and N. Mohammed, &quot;Curricular SincNet: Towards Robust Deep Speaker Recognition by Emphasizing Hard Samples in Latent Space,&quot; in BIOSIG 2021 - Proceedings of the 20th International Conference of the Biometrics Special Interest Group, 2021. doi: 10.1109/BIOSIG52210.2021.9548296. [CrossRef]</unstructured_citation></citation><citation key="ref7"><doi>10.1109/ACCESS.2020.2973541</doi><unstructured_citation>R. Jahangir et al., &quot;Text-Independent Speaker Identification through Feature Fusion and Deep Neural Network,&quot; IEEE Access, 2020, doi: 10.1109/ACCESS.2020.2973541. [CrossRef]</unstructured_citation></citation><citation key="ref8"><doi>10.1109/SSCI47803.2020.9308489</doi><unstructured_citation>S. Duraibi, W. Alhamdani, and F. T. Sheldon, &quot;Voice Feature Learning using Convolutional Neural Networks Designed to Avoid Replay Attacks,&quot; in 2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020, 2020. doi: 10.1109/SSCI47803.2020.9308489. [CrossRef]</unstructured_citation></citation><citation key="ref9"><doi>10.1016/j.gltp.2021.08.013</doi><unstructured_citation>D. R. KS, R. MD, and S. G, &quot;Comparative performance analysis for speech digit recognition based on MFCC and vector quantization,&quot; Glob. Transitions Proc., 2021, doi: 10.1016/j.gltp.2021.08.013. [CrossRef]</unstructured_citation></citation><citation key="ref10"><doi>10.32014/2020.2518-1467.137</doi><unstructured_citation>O. Mamyrbayev, A. Akhmediyarova, A. Kydyrbekova, N. O. Mekebayev, and B. Zhumazhanov, &quot;BIOMETRIC HUMAN AUTHENTICATION SYSTEM THROUGH SPEECH USING DEEP NEURAL NETWORKS (DNN),&quot; Bull., 2020, doi: 10.32014/2020.2518-1467.137. [CrossRef]</unstructured_citation></citation><citation key="ref11"><doi>10.1007/s12652-021-02960-0</doi><unstructured_citation>M. Dua, C. Jain, and S. Kumar, &quot;LSTM and CNN based ensemble approach for spoof detection task in automatic speaker verification systems,&quot; J. Ambient Intell. Humaniz. Comput., 2022, doi: 10.1007/s12652-021-02960-0. [CrossRef]</unstructured_citation></citation><citation key="ref12"><doi>10.18178/ijmlc.2019.9.2.778</doi><unstructured_citation>S. Bunrit, T. Inkian, N. Kerdprasop, and K. Kerdprasop, &quot;Text-independent speaker identification using deep learning model of convolution neural network,&quot; Int. J. Mach. Learn. Comput., 2019, doi: 10.18178/ijmlc.2019.9.2.778. [CrossRef]</unstructured_citation></citation><citation key="ref13"><doi>10.1080/23311916.2020.1751557</doi><unstructured_citation>K. Aizat, O. Mohamed, M. Orken, A. Ainur, and B. Zhumazhanov, &quot;Identification and authentication of user voice using DNN features and i-vector,&quot; Cogent Eng., 2020, doi: 10.1080/23311916.2020.1751557. [CrossRef]</unstructured_citation></citation><citation key="ref14"><doi>10.21437/Interspeech.2020-1955</doi><unstructured_citation>Q. Wang, P. Guo, and L. Xie, &quot;Inaudible adversarial perturbations for targeted attack in speaker recognition,&quot; in Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, 2020. doi: 10.21437/Interspeech.2020-1955. [CrossRef]</unstructured_citation></citation><citation key="ref15"><doi>10.1109/ICIT52682.2021.9491705</doi><unstructured_citation>S. Nasr, M. Quwaider, and R. Qureshi, &quot;Text-independent Speaker Recognition using Deep Neural Networks,&quot; in 2021 International Conference on Information Technology, ICIT 2021 - Proceedings, 2021. doi: 10.1109/ICIT52682.2021.9491705. [CrossRef]</unstructured_citation></citation><citation key="ref16"><doi>10.18280/jesa.540210</doi><unstructured_citation>G. HimaBindu, G. Lakshmeeswari, G. Lalitha, and P. P. S. Subhashini, &quot;Recognition using DNN with bacterial foraging optimization using MFCC coefficients,&quot; J. Eur. des Syst. Autom., 2021, doi: 10.18280/JESA.540210. [CrossRef]</unstructured_citation></citation><citation key="ref17"><doi>10.3390/app122111109</doi><unstructured_citation>Y. Kang, W. Kim, S. Lim, H. Kim, and H. Seo, &quot;DeepDetection: Privacy-Enhanced Deep Voice Detection and User Authentication for Preventing Voice Phishing,&quot; Appl. Sci., vol. 12, no. 21, p. 11109, 2022, doi: 10.3390/app122111109. [CrossRef]</unstructured_citation></citation><citation key="ref18"><doi>10.1109/TIFS.2020.3045937</doi><unstructured_citation>C. Z. Yang, J. Ma, S. Wang, and A. W. C. Liew, &quot;Preventing DeepFake Attacks on Speaker Authentication by Dynamic Lip Movement Analysis,&quot; IEEE Trans. Inf. Forensics Secur., 2021, doi: 10.1109/TIFS.2020.3045937. [CrossRef]</unstructured_citation></citation><citation key="ref19"><doi>10.1155/2022/5755785</doi><unstructured_citation>H. Park and T. Kim, &quot;User Authentication Method via Speaker Recognition and Speech Synthesis Detection,&quot; Secur. Commun. Networks, 2022, doi: 10.1155/2022/5755785. [CrossRef]</unstructured_citation></citation><citation key="ref20"><doi>10.1051/itmconf/20213701022</doi><unstructured_citation>K. Khadar Nawas, M. Kumar Barik, and A. Nayeemulla Khan, &quot;Speaker Recognition using Random Forest,&quot; ITM Web Conf., 2021, doi: 10.1051/itmconf/20213701022. [CrossRef]</unstructured_citation></citation><citation key="ref21"><doi>10.1007/s10772-021-09876-2</doi><unstructured_citation>A. Mittal and M. Dua, &quot;Automatic speaker verification systems and spoof detection techniques: review and analysis,&quot; Int. J. Speech Technol., 2022, doi: 10.1007/s10772-021-09876-2. [CrossRef]</unstructured_citation></citation><citation key="ref22"><doi>10.1109/TENCON.2019.8929592</doi><unstructured_citation>S. Debnath and P. Roy, &quot;Multi-modal authentication system based on audio-visual data,&quot; in IEEE Region 10 Annual International Conference, Proceedings/TENCON, 2019. doi: 10.1109/TENCON.2019.8929592. [CrossRef]</unstructured_citation></citation><citation key="ref23"><doi>10.1109/ANTS.2018.8710076</doi><unstructured_citation>V. Gujral, J. Joshi, P. Medikonda, and N. Grover, &quot;Advanced Speech Processing for Speaker Authentication in Communication Systems,&quot; in International Symposium on Advanced Networks and Telecommunication Systems, ANTS, 2018. doi: 10.1109/ANTS.2018.8710076. [CrossRef]</unstructured_citation></citation><citation key="ref24"><doi>10.3390/s19122730</doi><unstructured_citation>W. Jiang, Z. Wang, J. S. Jin, X. Han, and C. Li, &quot;Speech Emotion Recognition with Heterogeneous,&quot; pp. 1-15, 2019, doi: 10.3390/s19122730. [CrossRef]</unstructured_citation></citation><citation key="ref25"><doi>10.1109/WiSPNET.2016.7566366</doi><unstructured_citation>R. Jage and S. Upadhya, &quot;CELP and MELP speech coding techniques,&quot; Proc. 2016 IEEE Int. Conf. Wirel. Commun. Signal Process. Networking, WiSPNET 2016, pp. 1398-1402, 2016, doi: 10.1109/WiSPNET.2016.7566366. [CrossRef]</unstructured_citation></citation><citation key="ref26"><doi>10.1109/JSTSP.2017.2671435</doi><unstructured_citation>Z. Wu et al., &quot;ASVspoof: The Automatic Speaker Verification Spoofing and Countermeasures Challenge,&quot; IEEE J. Sel. Top. Signal Process., vol. PP, p. 1, 2017, doi: 10.1109/JSTSP.2017.2671435. [CrossRef]</unstructured_citation></citation><citation key="ref27"><doi>10.1155/2021/5559616</doi><unstructured_citation>R. T. Al-Hassani, D. C. Atilla, and Ç. Aydin, &quot;Development of High Accuracy Classifier for the Speaker Recognition System,&quot; Appl. Bionics Biomech., 2021, doi: 10.1155/2021/5559616. [CrossRef]</unstructured_citation></citation><citation key="ref28"><doi>10.3390/s22239309</doi><unstructured_citation>Z. Hao, J. Peng, X. Dang, H. Yan, and R. Wang, &quot;mmSafe: A Voice Security Verification System Based on Millimeter-Wave Radar,&quot; Sensors, vol. 22, no. 23, 2022, doi: 10.3390/s22239309. [CrossRef]</unstructured_citation></citation><citation key="ref29"><doi>10.1016/j.matpr.2021.04.075</doi><unstructured_citation>A. Tahseen Ali, H. S. Abdullah, and M. N. Fadhil, &quot;WITHDRAWN: Voice recognition system using machine learning techniques,&quot; Mater. Today Proc., 2021, doi: https://doi.org/10.1016/j.matpr.2021.04.075. [CrossRef]</unstructured_citation></citation><citation key="ref30"><doi>10.3390/s22218122</doi><unstructured_citation>A. B. Abdusalomov, F. Safarov, M. Rakhimov, B. Turaev, and T. K. Whangbo, &quot;Improved Feature Parameter Extraction from Speech Signals Using Machine Learning Algorithm,&quot; Sensors, vol. 22, no. 21, p. 8122, 2022, doi: 10.3390/s22218122. [CrossRef]</unstructured_citation></citation></citation_list>
</doi_citations>
</body>
</doi_batch>
