Relevant bibliographies by topics / Speaker recognition

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Speaker recognition'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Speaker recognition"

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Sun, Linhui, Yunyi Bu, Bo Zou, Sheng Fu, and Pingan Li. "Speaker Recognition Based on Fusion of a Deep and Shallow Recombination Gaussian Supervector." Electronics 10, no. 1 (2020): 20. http://dx.doi.org/10.3390/electronics10010020.

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Extracting speaker’s personalized feature parameters is vital for speaker recognition. Only one kind of feature cannot fully reflect the speaker’s personality information. In order to represent the speaker’s identity more comprehensively and improve speaker recognition rate, we propose a speaker recognition method based on the fusion feature of a deep and shallow recombination Gaussian supervector. In this method, the deep bottleneck features are first extracted by Deep Neural Network (DNN), which are used for the input of the Gaussian Mixture Model (GMM) to obtain the deep Gaussian supervecto
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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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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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Markowitz, Judith A. "Speaker recognition." Information Security Technical Report 3, no. 1 (1998): 14–20. http://dx.doi.org/10.1016/s1363-4127(98)80014-9.

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Markowitz, Judith A. "Speaker recognition." Information Security Technical Report 4 (January 1999): 28. http://dx.doi.org/10.1016/s1363-4127(99)80053-3.

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Furui, Sadaoki. "Speaker recognition." Scholarpedia 3, no. 4 (2008): 3715. http://dx.doi.org/10.4249/scholarpedia.3715.

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O'Shaughnessy, D. "Speaker recognition." IEEE ASSP Magazine 3, no. 4 (1986): 4–17. http://dx.doi.org/10.1109/massp.1986.1165388.

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Mannepalli, Kasiprasad, Suman Maloji, Panyam Narahari Sastry, Swetha Danthala, and Durgaprasad Mannepalli. "Text independent emotion recognition for Telugu speech by using prosodic features." International Journal of Engineering & Technology 7, no. 2.7 (2018): 594. http://dx.doi.org/10.14419/ijet.v7i2.7.10887.

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The human speech delivers different types of information about the speaker and speech. From the speech production side, the speech signal carries linguistic information such as the meaningful message and the language and emotional, geographical and the speaker’s physiological characteristics of the speaker information are conveyed. This paper focuses on automatically identifying the emotion of a speaker given a sample of speech. the speech signals considered in this work are collected from Telugu speakers. The features like pitch, pitch related prosody, energy and formants. The overall recogni
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Gonzalez-Rodriguez, Joaquin. "Evaluating Automatic Speaker Recognition systems: An overview of the NIST Speaker Recognition Evaluations (1996-2014)." Loquens 1, no. 1 (2014): e007. http://dx.doi.org/10.3989/loquens.2014.007.

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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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Dissertations / Theses on the topic "Speaker recognition"

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Chatzaras, Anargyros, and Georgios Savvidis. "Seamless speaker recognition." Thesis, KTH, Radio Systems Laboratory (RS Lab), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-159021.

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In a technologically advanced society, the average person manages dozens of accounts for e-mail, social networks, e-banking, and other electronic services. As the number of these accounts increases, the need for automatic user identification becomes more essential. Biometrics have long been used to identify people and are the most common (if not the only) method to achieve this task. Over the past few years, smartphones have become frequently used gadgets.  These devices have built-in microphones and are commonly used by a single user or a small set of users, such as a couple or a family. This
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VASILAKAKIS, VASILEIOS. "Forensic speaker recognition: speaker and height estimation techniques." Doctoral thesis, Politecnico di Torino, 2014. http://hdl.handle.net/11583/2551370.

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In this work, we analyse some techniques used to perform speaker verification, ex- plaining the steps from feature extraction to mathematical models used for speaker characterisation and discriminative modelling. The main contributions of the au- thor, is a modification on the i–vector generation process, making it either faster or less memory-demanding, a novel way to perform speaker verification by the use of the Pairwise Support Vector Machine and a new way to perform speaker characteri- sation by means of Deep belief networks. Apart from these contributions, additional work in Automatic Sp
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Kamarauskas, Juozas. "Speaker recognition by voice." Doctoral thesis, Lithuanian Academic Libraries Network (LABT), 2009. http://vddb.library.lt/obj/LT-eLABa-0001:E.02~2009~D_20090615_093847-20773.

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Questions of speaker’s recognition by voice are investigated in this dissertation. Speaker recognition systems, their evolution, problems of recognition, systems of features, questions of speaker modeling and matching used in text-independent and text-dependent speaker recognition are considered too. The text-independent speaker recognition system has been developed during this work. The Gaussian mixture model approach was used for speaker modeling and pattern matching. The automatic method for voice activity detection was proposed. This method is fast and does not require any additional acti
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Du, Toit Ilze. "Non-acoustic speaker recognition." Thesis, Stellenbosch : University of Stellenbosch, 2004. http://hdl.handle.net/10019.1/16315.

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Thesis (MScIng)--University of Stellenbosch, 2004.<br>ENGLISH ABSTRACT: In this study the phoneme labels derived from a phoneme recogniser are used for phonetic speaker recognition. The time-dependencies among phonemes are modelled by using hidden Markov models (HMMs) for the speaker models. Experiments are done using firstorder and second-order HMMs and various smoothing techniques are examined to address the problem of data scarcity. The use of word labels for lexical speaker recognition is also investigated. Single word frequencies are counted and the use of various word selections as
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Hong, Z. (Zimeng). "Speaker gender recognition system." Master's thesis, University of Oulu, 2017. http://jultika.oulu.fi/Record/nbnfioulu-201706082645.

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Abstract. Automatic gender recognition through speech is one of the fundamental mechanisms in human-machine interaction. Typical application areas of this technology range from gender-targeted advertising to gender-specific IoT (Internet of Things) applications. It can also be used to narrow down the scope of investigations in crime scenarios. There are many possible methods of recognizing the gender of a speaker. In machine learning applications, the first step is to acquire and convert the natural human voice into a form of machine understandable signal. Useful voice features then could be
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Al-Kilani, Menia. "Voice-signature-based Speaker Recognition." University of the Western Cape, 2017. http://hdl.handle.net/11394/5888.

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Magister Scientiae - MSc (Computer Science)<br>Personal identification and the protection of data are important issues because of the ubiquitousness of computing and these have thus become interesting areas of research in the field of computer science. Previously people have used a variety of ways to identify an individual and protect themselves, their property and their information. This they did mostly by means of locks, passwords, smartcards and biometrics. Verifying individuals by using their physical or behavioural fea
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Oglesby, J. "Neural models for speaker recognition." Thesis, Swansea University, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.638359.

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In recent years a resurgence of interest in neural modeling has taken place. This thesis examines one such class applied to the task of speaker recognition, with direct comparisons made to a contemporary approach based on vector quantisation (VQ). Speaker recognition systems in general, including feature representations and distance measures, are reviewed. The VQ approach, used for comparisons throughout the experimental work, is described in detail. Currently popular neural architectures are also reviewed and associated gradient-based training procedures examined. The performance of a VQ spea
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Thompson, J. "Speech variability in speaker recognition." Thesis, Swansea University, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.639230.

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This thesis is concerned with investigating the effects of variability on the automatic speaker recognition system performance. Both speaker generated variability and variability of the recording environment are examined. Speaker generated variability (intra-variation) has received less attention than variability of the recording environment, and is therefore the main focus of this thesis. In particular, of most concern is the intra-variation of data typically found in co-operative speaker recognition tasks. That is normally spoken speech, collected over a period of months. To assess the scale
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Mukherjee, Rishiraj. "Speaker Recognition Using Shifted MFCC." Scholar Commons, 2012. http://scholarcommons.usf.edu/etd/4136.

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Speaker Recognition is the art of recognizing a speaker from a given database using speech as the only input. In this thesis we will be discussing a novel approach to detect speakers. Here we will introduce the concept of shifted MFCC to add improvement over the performance from previous work which has shown quite a decent amount of accuracy of about 95% at best. We will be talking about adding different parameters which also contributed in improving the efficiency of speaker recognition. Also we will be testing our algorithm on Text dependent speech data and Text Independent speech data. Our
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Mwangi, Elijah. "Speaker independent isolated word recognition." Thesis, Loughborough University, 1987. https://dspace.lboro.ac.uk/2134/15425.

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The work presented in this thesis concerns the recognition of isolated words using a pattern matching approach. In such a system, an unknown speech utterance, which is to be identified, is transformed into a pattern of characteristic features. These features are then compared with a set of pre-stored reference patterns that were generated from the vocabulary words. The unknown word is identified as that vocabulary word for which the reference pattern gives the best match. One of the major difficul ties in the pattern comparison process is that speech patterns, obtained from the same word, exhi
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Books on the topic "Speaker recognition"

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Neustein, Amy, and Hemant A. Patil, eds. Forensic Speaker Recognition. Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-0263-3.

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Beigi, Homayoon. Fundamentals of Speaker Recognition. Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-77592-0.

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Lee, Chin-Hui, Frank K. Soong, and Kuldip K. Paliwal, eds. Automatic Speech and Speaker Recognition. Springer US, 1996. http://dx.doi.org/10.1007/978-1-4613-1367-0.

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Keshet, Joseph, and Samy Bengio, eds. Automatic Speech and Speaker Recognition. John Wiley & Sons, Ltd, 2009. http://dx.doi.org/10.1002/9780470742044.

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Zheng, Thomas Fang, and Lantian Li. Robustness-Related Issues in Speaker Recognition. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3238-7.

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Rao, K. Sreenivasa, and Sourjya Sarkar. Robust Speaker Recognition in Noisy Environments. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07130-5.

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Chin-Hui, Lee, Soong Frank K, and Paliwal K. K, eds. Automatic speech and speaker recognition: Advanced topics. Kluwer Academic Publishers, 1996.

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Irvine, David Alexander. A comparison of some speaker recognition techniques. The Author], 1992.

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Lee, Chin-Hui. Automatic Speech and Speaker Recognition: Advanced Topics. Springer US, 1996.

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Harkin, James G. Microprocessor based speaker-dependent isolated-word speech recognition. The Author], 1997.

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Book chapters on the topic "Speaker recognition"

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Zhang, David D. "Speaker Recognition." In Automated Biometrics. Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-4519-4_9.

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Farouk, Mohamed Hesham. "Speaker Recognition." In SpringerBriefs in Electrical and Computer Engineering. Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-02732-6_8.

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Beigi, Homayoon. "Speaker Recognition." In Fundamentals of Speaker Recognition. Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-77592-0_17.

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Furui, Sadaoki. "Speaker Recognition." In Computational Models of Speech Pattern Processing. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-60087-6_14.

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Beigi, Homayoon. "Speaker Recognition." In Encyclopedia of Cryptography and Security. Springer US, 2011. http://dx.doi.org/10.1007/978-1-4419-5906-5_747.

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Jain, Anil K., Arun A. Ross, Karthik Nandakumar, and Thomas Swearingen. "Speaker Recognition." In Texts in Computer Science. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-61675-4_6.

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Campbell, Joseph P. "Speaker Recognition." In Biometrics. Springer US, 1996. http://dx.doi.org/10.1007/0-306-47044-6_8.

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Somogyi, Zoltán. "Speaker Recognition." In The Application of Artificial Intelligence. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-60032-7_7.

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Beigi, Homayoon. "Speaker Recognition." In Encyclopedia of Cryptography, Security and Privacy. Springer Berlin Heidelberg, 2021. http://dx.doi.org/10.1007/978-3-642-27739-9_747-2.

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Beigi, Homayoon. "Speaker Recognition." In Encyclopedia of Cryptography, Security and Privacy. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-030-71522-9_747.

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Conference papers on the topic "Speaker recognition"

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Shantoash, C., M. Vishal, S. Shruthi, and Gopalsamy N. Bharathi. "Speech Accent Recognition." In International Research Conference on IOT, Cloud and Data Science. Trans Tech Publications Ltd, 2023. http://dx.doi.org/10.4028/p-irai1l.

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The speech accent demonstrates that accents are systematic instead of merely mistaken speech. This project allows detecting the demographic and linguistic backgrounds of the speakers by comparing different speech outputs with the speech accent archive dataset to work out which variables are key predictors of every accent. Given a recording of a speaker speaking a known script of English words, this project predicts the speaker’s language. This project aims to classify various sorts of accents, specifically foreign accents, by the language of the speaker. This project revolves round the detecti
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Bao, Yinan, Qianwen Ma, Lingwei Wei, Wei Zhou, and Songlin Hu. "Speaker-Guided Encoder-Decoder Framework for Emotion Recognition in Conversation." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/562.

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The emotion recognition in conversation (ERC) task aims to predict the emotion label of an utterance in a conversation. Since the dependencies between speakers are complex and dynamic, which consist of intra- and inter-speaker dependencies, the modeling of speaker-specific information is a vital role in ERC. Although existing researchers have proposed various methods of speaker interaction modeling, they cannot explore dynamic intra- and inter-speaker dependencies jointly, leading to the insufficient comprehension of context and further hindering emotion prediction. To this end, we design a no
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Tripathi, Supriya, and Smriti Bhatnagar. "Speaker Recognition." In 2012 3rd International Conference on Computer and Communication Technology (ICCCT 2012). IEEE, 2012. http://dx.doi.org/10.1109/iccct.2012.64.

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Wang, Zhong-Qiu, Anurag Kumar, and Shinji Watanabe. "Cross-Talk Reduction." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/572.

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While far-field multi-talker mixtures are recorded, each speaker can wear a close-talk microphone so that close-talk mixtures can be recorded at the same time. Although each close-talk mixture has a high signal-to-noise ratio (SNR) of the wearer, it has a very limited range of applications, as it also contains significant cross-talk speech by other speakers and is not clean enough. In this context, we propose a novel task named \textit{cross-talk reduction} (CTR) which aims at reducing cross-talk speech, and a novel solution named CTRnet which is based on unsupervised or weakly-supervised neur
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Doddington, George. "Speaker recognition based on idiolectal differences between speakers." In 7th European Conference on Speech Communication and Technology (Eurospeech 2001). ISCA, 2001. http://dx.doi.org/10.21437/eurospeech.2001-417.

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Kohler, M. A., W. D. Andrews, J. P. Campbell, and J. Herndndez-Cordero. "Phonetic speaker recognition." In Conference Record. Thirty-Fifth Asilomar Conference on Signals, Systems and Computers. IEEE, 2001. http://dx.doi.org/10.1109/acssc.2001.987748.

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Desai, Veena, and Hema A. Murthy. "Distributed speaker recognition." In Interspeech 2004. ISCA, 2004. http://dx.doi.org/10.21437/interspeech.2004-536.

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Wenndt, Stanley J., and Ronald L. Mitchell. "Familiar speaker recognition." In ICASSP 2012 - 2012 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2012. http://dx.doi.org/10.1109/icassp.2012.6288854.

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Yu, Kin, John S. Mason, and John Oglesby. "Speaker recognition models." In 4th European Conference on Speech Communication and Technology (Eurospeech 1995). ISCA, 1995. http://dx.doi.org/10.21437/eurospeech.1995-105.

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Andrews, Walter D., Mary A. Kohler, and Joseph P. Campbell. "Phonetic speaker recognition." In 7th European Conference on Speech Communication and Technology (Eurospeech 2001). ISCA, 2001. http://dx.doi.org/10.21437/eurospeech.2001-416.

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Reports on the topic "Speaker recognition"

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Slyh, Raymond E., Eric G. Hansen, and Timothy R. Anderson. AFRL/HECP Speaker Recognition Systems for the 2004 NIST Speaker Recognition Evaluation. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada430750.

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Wu, Jin Chu, Alvin F. Martin, Craig S. Greenberg, and Raghu N. Kacker. Measurement uncertainties in speaker recognition evaluation. National Institute of Standards and Technology, 2010. http://dx.doi.org/10.6028/nist.ir.7722.

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Quatieri, T. F., E. Singer, R. B. Dunn, D. A. Reynolds, and J. P. Campbell. Speaker and Language Recognition Using Speech Codec Parameters. Defense Technical Information Center, 1999. http://dx.doi.org/10.21236/ada526525.

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Martinson, E., and W. Lawson. Learning Speaker Recognition Models through Human-Robot Interaction. Defense Technical Information Center, 2011. http://dx.doi.org/10.21236/ada550036.

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Hansen, John H. Robust Speech Processing & Recognition: Speaker ID, Language ID, Speech Recognition/Keyword Spotting, Diarization/Co-Channel/Environmental Characterization, Speaker State Assessment. Defense Technical Information Center, 2015. http://dx.doi.org/10.21236/ada623029.

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Quatieri, T. F. Nonlinear Auditory Modeling as a Basis for Speaker Recognition. Defense Technical Information Center, 2002. http://dx.doi.org/10.21236/ada402327.

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Wu, Jin Chu, Alvin F. Martin, Craig S. Greenberg, and Raghu N. Kacker. Data dependency on measurement uncertainties in speaker recognition evaluation. National Institute of Standards and Technology, 2011. http://dx.doi.org/10.6028/nist.ir.7810.

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Cieri, Christopher, Joseph P. Campbell, Hirotaka Nakasone, Kevin Walker, and David Miller. The Mixer Corpus of Multilingual, Multichannel Speaker Recognition Data. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada523534.

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Karakowski, Joseph A., and Hai H. Phu. Text Independent Speaker Recognition Using A Fuzzy Hypercube Classifier. Defense Technical Information Center, 1998. http://dx.doi.org/10.21236/ada354792.

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Ferrer, Luciana, Mitchell McLaren, Nicolas Scheffer, Yun Lei, Martin Graciarena, and Vikramjit Mitra. A Noise-Robust System for NIST 2012 Speaker Recognition Evaluation. Defense Technical Information Center, 2013. http://dx.doi.org/10.21236/ada614010.

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