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Journal articles on the topic 'Speaker identification'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Shah, Shahid Munir, Muhammad Moinuddin, and Rizwan Ahmed Khan. "A Robust Approach for Speaker Identification Using Dialect Information." Applied Computational Intelligence and Soft Computing 2022 (March 7, 2022): 1–16. http://dx.doi.org/10.1155/2022/4980920.

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The present research is an effort to enhance the performance of voice processing systems, in our case the speaker identification system (SIS) by addressing the variability caused by the dialectical variations of a language. We present an effective solution to reduce dialect-related variability from voice processing systems. The proposed method minimizes the system’s complexity by reducing search space during the testing process of speaker identification. The speaker is searched from the set of speakers of the identified dialect instead of all the speakers present in system training. The study

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Jayanna, H. S., and B. G. Nagaraja. "An Experimental Comparison of Modeling Techniques and Combination of Speaker – Specific Information from Different Languages for Multilingual Speaker Identification." Journal of Intelligent Systems 25, no. 4 (2016): 529–38. http://dx.doi.org/10.1515/jisys-2014-0128.

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AbstractMost of the state-of-the-art speaker identification systems work on a monolingual (preferably English) scenario. Therefore, English-language autocratic countries can use the system efficiently for speaker recognition. However, there are many countries, including India, that are multilingual in nature. People in such countries have habituated to speak multiple languages. The existing speaker identification system may yield poor performance if a speaker’s train and test data are in different languages. Thus, developing a robust multilingual speaker identification system is an issue in ma

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Aung, Zaw Win. "A Robust Speaker Identification System." International Journal of Trend in Scientific Research and Development Volume-2, Issue-5 (2018): 2057–64. http://dx.doi.org/10.31142/ijtsrd18274.

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Mehendale, Arundhati S., and M. R. Dixit. "Speaker Identification." Signal & Image Processing : An International Journal 2, no. 2 (2011): 62–69. http://dx.doi.org/10.5121/sipij.2011.2206.

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Singh, Mahesh K., P. Mohana Satya, Vella Satyanarayana, and Sridevi Gamini. "Speaker Recognition Assessment in a Continuous System for Speaker Identification." International Journal of Electrical and Electronics Research 10, no. 4 (2022): 862–67. http://dx.doi.org/10.37391/ijeer.100418.

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This research article presented and focused on recognizing speakers through multi-speaker speeches. The participation of several speakers includes every conference, talk or discussion. This type of talk has different problems as well as stages of processing. Challenges include the unique impurity of the surroundings, the involvement of speakers, speaker distance, microphone equipment etc. In addition to addressing these hurdles in real time, there are also problems in the treatment of the multi-speaker speech. Identifying speech segments, separating the speaking segments, constructing clusters

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ZHOU, GUANGYU, WASFY B. MIKHAEL, and BRENT MYERS. "NOVEL DISCRIMINATIVE VECTOR QUANTIZATION APPROACH FOR SPEAKER IDENTIFICATION." Journal of Circuits, Systems and Computers 14, no. 03 (2005): 581–96. http://dx.doi.org/10.1142/s0218126605002404.

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A novel Discriminative Vector Quantization method for Speaker Identification (DVQSI) is proposed, and its parameters selection is discussed. In the training mode of this approach, the vector space of speech features is divided into a number of regions. Then, a Vector Quantization (VQ) codebook for each speaker in each region is constructed. For every possible combination of speaker pairs, a discriminative weight is assigned for each region, based on the region's ability to discriminate between the speaker pair. Consequently, the region, which contains a larger distribution difference between t

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Kim, Kyung-Wha, Byung-Min So, and Ha-Jin Yu. "Forensic Automatic Speaker Identification System for Korean Speakers." Phonetics and Speech Sciences 4, no. 3 (2012): 95–101. http://dx.doi.org/10.13064/ksss.2012.4.3.095.

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Bakst, Sarah, Ebony Pearson, Luciana Ferrer, Mitchell McLaren, and Aaron Lawson. "A phonetic basis of accent bias in speaker identification technology." Journal of the Acoustical Society of America 156, no. 4_Supplement (2024): A103. https://doi.org/10.1121/10.0035253.

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Speaker identification (SID) technology aims to determine whether the speech in a recording of an unknown speaker matches that of speaker known to the SID system. If the unknown speaker comes from a community that is underrepresented in the SID training data (e.g., accent), the SID model is more likely to confuse the unknown speaker for other speakers in that speaker community: group-level characteristics are mistaken for individual identifiers (all speakers with that accent seem like the same speaker to SID). Previous solutions include reweighting training data to create balance across speake

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Mahmood, Suzan A., and Loay E. George. "Speaker Identification Using Backpropagation Neural Network." Journal of Zankoy Sulaimani - Part A 11, no. 1 (2007): 61–66. http://dx.doi.org/10.17656/jzs.10181.

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Lakshmi Prasanna, P. "Attention for the speech of cleft lip and palate in speaker recognition." Open Journal of Pain Medicine 7, no. 1 (2023): 7–1. http://dx.doi.org/10.17352/ojpm.000036.

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Artificial Intelligence (AI) has become indispensable to all people, primarily for the purposes of speaker recognition, voice identification, educational purposes, workplace, and health care. Based on a speaker’s voice characteristics, identification and recognition of the speaker is accomplished. The voice is affected by both intra- and interspeaker variability. In addition to this, a condition known as structural abnormalities can cause resonance, which can seriously affect voice quality. As a result, speakers may experience difficulties when using AI-based devices. The study aims to investi

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Basak, Gopal K., and Tridibesh Dutta. "Statistical Speaker Identification Based on Spectrogram Imaging." Calcutta Statistical Association Bulletin 59, no. 3-4 (2007): 253–63. http://dx.doi.org/10.1177/0008068320070309.

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Abstract: The paper addresses the problem of speaker identification based on spectrograms in the text dependent case. Using spectrogram segmentation, this paper, mainly, focusses on understanding the complex patterns in frequency and amplitude in an utterance of a given word by an individual. The features used for identifying a speaker based on an observed variable extracted from the spectrograms, rely on the distinct speaker effect, his/her interaction effect with the particular word and with the frequency bands of the spectrogram. Performance of this novel approach on spectrogram samples, co

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Yarmey, A. Daniel. "Earwitness speaker identification." Psychology, Public Policy, and Law 1, no. 4 (1995): 792–816. http://dx.doi.org/10.1037/1076-8971.1.4.792.

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EhKan, Phaklen, Timothy Allen, and Steven F. Quigley. "FPGA Implementation for GMM-Based Speaker Identification." International Journal of Reconfigurable Computing 2011 (2011): 1–8. http://dx.doi.org/10.1155/2011/420369.

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In today's society, highly accurate personal identification systems are required. Passwords or pin numbers can be forgotten or forged and are no longer considered to offer a high level of security. The use of biological features, biometrics, is becoming widely accepted as the next level for security systems. Biometric-based speaker identification is a method of identifying persons from their voice. Speaker-specific characteristics exist in speech signals due to different speakers having different resonances of the vocal tract. These differences can be exploited by extracting feature vectors su

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Kim, Myung-Jae, Il-Ho Yang, Byung-Min So, Min-Seok Kim, and Ha-Jin Yu. "Histogram Equalization Using Background Speakers' Utterances for Speaker Identification." Phonetics and Speech Sciences 4, no. 2 (2012): 79–86. http://dx.doi.org/10.13064/ksss.2012.4.2.079.

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Chauhan, Neha, Tsuyoshi Isshiki, and Dongju Li. "Enhancing Speaker Recognition Models with Noise-Resilient Feature Optimization Strategies." Acoustics 6, no. 2 (2024): 439–69. http://dx.doi.org/10.3390/acoustics6020024.

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This paper delves into an in-depth exploration of speaker recognition methodologies, with a primary focus on three pivotal approaches: feature-level fusion, dimension reduction employing principal component analysis (PCA) and independent component analysis (ICA), and feature optimization through a genetic algorithm (GA) and the marine predator algorithm (MPA). This study conducts comprehensive experiments across diverse speech datasets characterized by varying noise levels and speaker counts. Impressively, the research yields exceptional results across different datasets and classifiers. For i

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Vančura, Alma, and Filip Alić. "Students’ identification of different English varieties." Govor/Speech 39, no. 1 (2022): 19–38. http://dx.doi.org/10.22210/govor.2022.39.02.

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Today’s technology allows quick and easy communication with speakers from a variety of language backgrounds, and the communication of online participants is predominantly in English. Although much is already known about the attitudes of Croatian students towards their own English pronunciation (e.g., Lütze-Miculinić, 2019; Josipović Smojver & Stanojević, 2013, 2016; Stanojević & Josipović Smojver, 2011) or about different English varieties (Drljača Margić & Širola, 2014), there has been no research regarding students’ identification of different English varieties in Croatian contex

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Singh, Mahesh K., S. Manusha, K. V. Balaramakrishna, and Sridevi Gamini. "Speaker Identification Analysis Based on Long-Term Acoustic Characteristics with Minimal Performance." International Journal of Electrical and Electronics Research 10, no. 4 (2022): 848–52. http://dx.doi.org/10.37391/ijeer.100415.

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The identity of the speakers depends on the phonological properties acquired from the speech. The Mel-Frequency Cepstral Coefficients (MFCC) are better researched for derived the acoustic characteristic. This speaker model is based on a small representation and the characteristics of the acoustic features. These are derived from the speaker model and the cartographic representation by the MFCCs. The MFCC is used for independent monitoring of speaker text. There is a problem with the recognition of speakers by small representation, so proposed the Gaussian Mixture Model (GMM), mean super vector

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Lambamo, Wondimu, Ramasamy Srinivasagan, Worku Jifara, and Ali Alzahrani. "Speaker identification under noisy conditions using hybrid convolutional neural network and gated recurrent unit." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 1 (2024): 1050–62. https://doi.org/10.11591/ijai.v13.i1.pp1050-1062.

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Speaker identification is biometrics that classifies or identifies a person from other speakers based on speech characteristics. Recently, deep learning models outperformed conventional machine learning models in speaker identification. Spectrograms of the speech have been used as input in deep learning-based speaker identification using clean speech. However, the performance of speaker identification systems gets degraded under noisy conditions. Cochleograms have shown better results than spectrograms in deep learning-based speaker recognition under noisy and mismatched conditions. Moreover,

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Husam, Husam, and Husam Ali Abdulmohsin. "Speaker Identification in Crowd Speech Audio using Convolutional Neural Networks." Fusion: Practice and Applications 16, no. 2 (2024): 118–25. http://dx.doi.org/10.54216/fpa.160208.

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Crowd speaker identification is the most advanced technology in the field of audio identification and personal user experience which researchers have extensively focused on, but still, science hasn’t been able to achieve high results in crowed identification. This work aims to design and implement a novel crowd speech identification method that can identify speakers in a multi speaker environment, (two, three, four and five speakers). This work will be implemented through two phases. The training phase is the Convolutional Neural Network (CNN) training and testing phase. Through this phase, th

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Nursholihatun, Erina, Sudi Mariyanto Sasongko, and Abdullah Zainuddin. "IDENTIFIKASI SUARA MENGGUNAKAN METODE MEL FREQUENCY CEPSTRUM COEFFICIENTS (MFCC) DAN JARINGAN SYARAF TIRUAN BACKPROPAGATION." DIELEKTRIKA 7, no. 1 (2020): 48. http://dx.doi.org/10.29303/dielektrika.v7i1.232.

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The voice is basic humans tool of communications. Speakers identifications is the process of recoqnizing the identity of a speaker by comparing the inputed voice features with all the features of each speaker in the database.There are two step of speaker identification process: feature extraction and pattern recognition. For the characteristic extraction phase using Mel Frequency Cepstrum Coefficient (MFCC) method. The method of pattern recognition using backpropagation artificial neural networks that compares the test data with the reference data in the database based on the variable result i

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Alkhatib, Bassel, and Mohammad Madian Waleed Kamal Eddin. "Voice Identification Using MFCC and Vector Quantization." Baghdad Science Journal 17, no. 3(Suppl.) (2020): 1019. http://dx.doi.org/10.21123/bsj.2020.17.3(suppl.).1019.

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The speaker identification is one of the fundamental problems in speech processing and voice modeling. The speaker identification applications include authentication in critical security systems and the accuracy of the selection. Large-scale voice recognition applications are a major challenge. Quick search in the speaker database requires fast, modern techniques and relies on artificial intelligence to achieve the desired results from the system. Many efforts are made to achieve this through the establishment of variable-based systems and the development of new methodologies for speaker ident

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Almarshady, Nourah M., Adal A. Alashban, and Yousef A. Alotaibi. "Analysis and Investigation of Speaker Identification Problems Using Deep Learning Networks and the YOHO English Speech Dataset." Applied Sciences 13, no. 17 (2023): 9567. http://dx.doi.org/10.3390/app13179567.

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The rapid momentum of deep neural networks (DNNs) in recent years has yielded state-of-the-art performance in various machine-learning tasks using speaker identification systems. Speaker identification is based on the speech signals and the features that can be extracted from them. In this article, we proposed a speaker identification system using the developed DNNs models. The system is based on the acoustic and prosodic features of the speech signal, such as pitch frequency (vocal cords vibration rate), energy (loudness of speech), their derivations, and any additional acoustic and prosodic

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Gish, H., and M. Schmidt. "Text-independent speaker identification." IEEE Signal Processing Magazine 11, no. 4 (1994): 18–32. http://dx.doi.org/10.1109/79.317924.

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Hollien, Harry. "Update on Speaker Identification." Contemporary Psychology: A Journal of Reviews 30, no. 10 (1985): 801–2. http://dx.doi.org/10.1037/023262.

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Jayanna, H. S., and S. R. Mahadeva Prasanna. "Limited data speaker identification." Sadhana 35, no. 5 (2010): 525–46. http://dx.doi.org/10.1007/s12046-010-0043-8.

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Foulkes, Paul, India Smith, and Márton Sóskuthy. "Speaker Identification in Whisper." Letras de Hoje 52, no. 1 (2017): 5. http://dx.doi.org/10.15448/1984-7726.2017.1.26659.

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Sociophonetic methods and findings have value in application to real-life issues, including providing expert forensic evidence in legal cases. Forensic cases often involve voices which differ markedly from those typically encountered in laboratory or field studies. We assess the ability of people to identify familiar voices produced in whisper, a commonly used form of disguise. Members of a pre-existing social network were recorded speaking normally and in whisper. Speakers found it difficult to maintain whisper beyond 30 seconds. They and other members of the group listened to extracts that w

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Shertayev, K. A., та L. K. Naizabayeva. "ГЛУБОКОЕ ОБУЧЕНИЕ В ИДЕНТИФИКАЦИИ СПИКЕРА: СОВРЕМЕННЫЕ МЕТОДЫ И ПЕРСПЕКТИВЫ РАЗВИТИЯ//МЕЖДУНАРОДНЫЙ ЖУРНАЛ ИНФОРМАЦИОННЫХ И КОММУНИКАЦИОННЫХ ТЕХНОЛОГИЙ." INTERNATIONAL JOURNAL OF INFORMATION AND COMMUNICATION TECHNOLOGIES 5, № 1(17) (2024): 98–109. http://dx.doi.org/10.54309/ijict.2024.17.1.008.

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The article contains a review of the research on the use of deep learning in speaker identification. It examines the problems of voice identification, highlighting the relevance and the need for effective methods in this area. The evolution of speaker identification techniques from simple pattern matching to complex neural architectures is traced to understand the technological advancements in this field. Modern methods for speaker identification and the prospects for the development of such systems are considered. The two aims set by the authors are: to make comparative analysis of deep learn

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Zaw, Win Aung. "A Robust Speaker Identification System." International Journal of Trend in Scientific Research and Development 2, no. 5 (2018): 2057–64. https://doi.org/10.31142/ijtsrd18274.

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This paper is aimed to implement a robust speaker identification system. It is a software architecture which identifies the current talker out of a set of speakers. The system is emphasized on text dependent speaker identification system. It contains three main modules endpoint detection, feature extraction and feature matching. The additional module, endpoint detection, removes unwanted signal and background noise from the input speech signal before subsequent processing. In the proposed system, Short Term Energy analysis is used for endpoint detection. Mel frequency Cepstrum Coefficients MFC

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Journal, Baghdad Science. "Using Neural Network with Speaker Applications." Baghdad Science Journal 7, no. 2 (2010): 1076–81. http://dx.doi.org/10.21123/bsj.7.2.1076-1081.

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In Automatic Speech Recognition (ASR) the non-linear data projection provided by a one hidden layer Multilayer Perceptron (MLP), trained to recognize phonemes, and has previous experiments to provide feature enhancement substantially increased ASR performance, especially in noise. Previous attempts to apply an analogous approach to speaker identification have not succeeded in improving performance, except by combining MLP processed features with other features. We present test results for the TIMIT database which show that the advantage of MLP preprocessing for open set speaker identification

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Mazher, Alaa noori, and Samira faris Khlibs. "Using Neural Network with Speaker Applications." Baghdad Science Journal 7, no. 2 (2010): 1076–81. http://dx.doi.org/10.21123/bsj.2010.7.2.1076-1081.

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In Automatic Speech Recognition (ASR) the non-linear data projection provided by a one hidden layer Multilayer Perceptron (MLP), trained to recognize phonemes, and has previous experiments to provide feature enhancement substantially increased ASR performance, especially in noise. Previous attempts to apply an analogous approach to speaker identification have not succeeded in improving performance, except by combining MLP processed features with other features. We present test results for the TIMIT database which show that the advantage of MLP preprocessing for open set speaker identification

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Dwijayanti, Suci, Alvio Yunita Putri, and Bhakti Yudho Suprapto. "Speaker Identification Using a Convolutional Neural Network." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 6, no. 1 (2022): 140–45. http://dx.doi.org/10.29207/resti.v6i1.3795.

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Speech, a mode of communication between humans and machines, has various applications, including biometric systems for identifying people have access to secure systems. Feature extraction is an important factor in speech recognition with high accuracy. Therefore, we implemented a spectrogram, which is a pictorial representation of speech in terms of raw features, to identify speakers. These features were inputted into a convolutional neural network (CNN), and a CNN-visual geometry group (CNN-VGG) architecture was used to recognize the speakers. We used 780 primary data from 78 speakers, and ea

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Khoma, Volodymyr, Yuriy Khoma, Vitalii Brydinskyi, and Alexander Konovalov. "Development of Supervised Speaker Diarization System Based on the PyAnnote Audio Processing Library." Sensors 23, no. 4 (2023): 2082. http://dx.doi.org/10.3390/s23042082.

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Diarization is an important task when work with audiodata is executed, as it provides a solution to the problem related to the need of dividing one analyzed call recording into several speech recordings, each of which belongs to one speaker. Diarization systems segment audio recordings by defining the time boundaries of utterances, and typically use unsupervised methods to group utterances belonging to individual speakers, but do not answer the question “who is speaking?” On the other hand, there are biometric systems that identify individuals on the basis of their voices, but such systems are

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Šešum, Mia, Bojana Drljan, Maja Ivanović, and Ivana Arsenić. "Speaker recognition based on auditory impression: The role of familiarity with the speaker and language." Nauka bezbednost policija 30, no. 2 (2025): 110–26. https://doi.org/10.5937/nabepo30-56871.

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Speech is a fundamental means of interpersonal communication. Speaker identification based on voice and speech can be analysed through two perspectives, expert listening by trained phoneticians, for the purpose of forensic speaker identification, and by naive listeners. Factors related to the success of identification often include prior familiarity with the speaker and the language they speak. The aim of the study is to examine whether speaker recognition based on auditory impressions is influenced by prior familiarity with the speaker and the language being spoken. A total of 218 female stud

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Anito, Wondimu Lambamo, Ramasamy Srinivasagan, Worku Jifara, and Ali Alzahrani. "Speaker identification under noisy conditions using hybrid convolutional neural network and gated recurrent unit." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 1 (2024): 1050. http://dx.doi.org/10.11591/ijai.v13.i1.pp1050-1062.

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<p><span>Speaker identification is biometrics that classifies or identifies a person from other speakers based on speech characteristics. Recently, deep learning models outperformed conventional machine learning models in speaker identification. Spectrograms of the speech have been used as input in deep learning-based speaker identification using clean speech. However, the performance of speaker identification systems gets degraded under noisy conditions. Cochleograms have shown better results than spectrograms in deep learning-based speaker recognition under noisy and mismatched c

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Lin, Yu-Jung, Joshua Isakson, and Emma Keane. "Impact of face masks on second language word identification." Journal of the Acoustical Society of America 151, no. 4 (2022): A277—A278. http://dx.doi.org/10.1121/10.0011331.

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The current study investigated the effects of face masks on the intelligibility of second language (L2) speech. Specifically, we examined whether L2 learners of Mandarin and English identify words in their L2s less accurately when the speakers spoke through masks. Seven Mandarin native speakers whose L2 is English and seven English native speakers whose L2 is Mandarin were asked to identify the words they heard in videos, where English and Mandarin native speakers pronounced monosyllabic words in their native languages with and without surgical masks. The first languages (L1s) of these 14 subj

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Leila, Beltaifa Zouari *1 Asma Chayeh 2. "SPEAKER RECOGNITION OF MAGHREB DIALECTS." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 6, no. 11 (2017): 413–21. https://doi.org/10.5281/zenodo.1066198.

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A few studies have focused on the west Arabic (Maghreb) dialects for which resources are rare. To handle this problem, we devoped a web-based database of speech from Tunisian, Algerian and Moroccan speakers covering the diversity of Arabic dialects spoken in north Africa. Then speaker identification and verification experiments have been conducted in order to evaluate the performance of each dialect-based system. A baseline system using Timit database have also be developed for comparison purposes. The experiments show that the performances of dialectal speaker identification systems outperfor

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Reynolds, Douglas A. "Speaker identification and verification using Gaussian mixture speaker models." Speech Communication 17, no. 1-2 (1995): 91–108. http://dx.doi.org/10.1016/0167-6393(95)00009-d.

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Et. al., ShashiRanjan. "Voice Biometric: A Novel and Realistic Approach." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 3 (2021): 5684–94. http://dx.doi.org/10.17762/turcomat.v12i3.2243.

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Speaker identification uses the basic speaker's wave information to recognize the speaker. The device validates the speaker's identity, which makes the person eligible for the various services the voice can provide. This will fortify every device. Attributed voice is an algorithm focused on a speaker's physiological and behavioral characteristics. Speech analysis provides it with the distinguishing characteristics of identity, allowing the speaker to be distinguished from the others.

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Kumar Pentapati, Hema, and Sridevi K. "Enhancement in Speaker Identification through Feature Fusion using Advanced Dilated Convolution Neural Network." International journal of electrical and computer engineering systems 14, no. 3 (2023): 301–10. http://dx.doi.org/10.32985/ijeces.14.3.8.

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There are various challenges in identifying the speakers accurately. The Extraction of discriminative features is a vital task for accurate identification in the speaker identification task. Nowadays, speaker identification is widely investigated using deep learning. The complex and noisy speech data affects the performance of Mel Frequency Cepstral Coefficients (MFCC); hence, MFCC fails to represent the speaker characteristics accurately. In this proposed work, a novel text-independent speaker identification system is developed to enhance the performance by fusion of Log-MelSpectrum and excit

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Singh, Satyanand. "Forensic and Automatic Speaker Recognition System." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 5 (2018): 2804. http://dx.doi.org/10.11591/ijece.v8i5.pp2804-2811.

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<span lang="EN-US">Current Automatic Speaker Recognition (ASR) System has emerged as an important medium of confirmation of identity in many businesses, ecommerce applications, forensics and law enforcement as well. Specialists trained in criminological recognition can play out this undertaking far superior by looking at an arrangement of acoustic, prosodic, and semantic attributes which has been referred to as structured listening. An algorithmbased system has been developed in the recognition of forensic speakers by physics scientists and forensic linguists to reduce the probability of

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Kamiński, Kamil A., and Andrzej P. Dobrowolski. "Automatic Speaker Recognition System Based on Gaussian Mixture Models, Cepstral Analysis, and Genetic Selection of Distinctive Features." Sensors 22, no. 23 (2022): 9370. http://dx.doi.org/10.3390/s22239370.

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This article presents the Automatic Speaker Recognition System (ASR System), which successfully resolves problems such as identification within an open set of speakers and the verification of speakers in difficult recording conditions similar to telephone transmission conditions. The article provides complete information on the architecture of the various internal processing modules of the ASR System. The speaker recognition system proposed in the article, has been compared very closely to other competing systems, achieving improved speaker identification and verification results, on known cer

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Jie, Lim Jun, Muhammad Mun’im Ahmad Zabidi, Shahidatul Sadiah, and Ab Al-Hadi Ab Rahman. "Siamese Networks for Speaker Identification on Resource-Constrained Platforms." Journal of Physics: Conference Series 2622, no. 1 (2023): 012014. http://dx.doi.org/10.1088/1742-6596/2622/1/012014.

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Abstract This paper investigates the implementation of a lightweight Siamese neural network for enhancing speaker identification accuracy and inference speed in embedded systems. Integrating speaker identification into embedded systems can improve portability and versatility. Siamese neural networks achieve speaker identification by comparing input voice samples to reference voices in a database, effectively extracting features and classifying speakers accurately. Considering the trade-off between accuracy and complexity, as well as hardware constraints in embedded systems, various neural netw

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Abd El-Wahab, Basant S., Heba A. El-khobby, Mustafa M. Abd Elnaby, and Fathi E. Abd El-Samie. "Simultaneous speaker identification and watermarking." International Journal of Speech Technology 24, no. 1 (2021): 205–18. http://dx.doi.org/10.1007/s10772-019-09658-x.

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J. Stephan, Jane. "Speaker Identification Using Evolutionary Algorithm." Research Journal of Applied Sciences, Engineering and Technology 13, no. 9 (2016): 717–21. http://dx.doi.org/10.19026/rjaset.13.3345.

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Viswanadham, Y. K., T. V. Subrahmanyam, and I. Leela Priya. "Pass-Phrase based Speaker Identification." International Journal of Computer Applications 10, no. 8 (2010): 6–9. http://dx.doi.org/10.5120/1504-2022.

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Li, Qiang, and Yan Hong Liu. "SVM-GMM Based Speaker Identification." Advanced Materials Research 1044-1045 (October 2014): 1370–74. http://dx.doi.org/10.4028/www.scientific.net/amr.1044-1045.1370.

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Abstract:

Although a great success has been achieved under the environment of lab where the training data is sufficient and the surroundings are quiet, speaker identification (SI) in practical use still remains a challenge because of the complicated environment. To tackle this challenge, a hybrid system of Gaussian mixture model-support vector machines (GMM-SVM) is proposed in this paper. SVM can do well with less data but is computationally expensive while GMM is computationally inexpensive but needs more data to perform adequately. In this paper, SVM and GMM are parallel in both the training and testi

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Gill, Manjot Kaur, Reetkamal Kaur, and Jagdev Kaur. "Vector Quantization based Speaker Identification." International Journal of Computer Applications 4, no. 2 (2010): 1–4. http://dx.doi.org/10.5120/806-1146.

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Khan, Atif, Vikas Kumar, and Santosh Kumar. "Speaker identification using fuzzy logic." Invertis Journal of Science & Technology 10, no. 4 (2017): 209. http://dx.doi.org/10.5958/2454-762x.2017.00033.6.

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Yarmey, A. Daniel. "Earwitness descriptions and speaker identification." Forensic Linguistics 8, no. 1 (2001): 113–22. http://dx.doi.org/10.1558/sll.2001.8.1.113.

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Jessen, Michael. "Review article: Forensic Speaker Identification." Forensic Linguistics 10, no. 1 (2003): 138–51. http://dx.doi.org/10.1558/sll.2003.10.1.138.

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