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Relevant bibliographies by topics / Automatic speaker recognition

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Academic literature on the topic 'Automatic speaker recognition'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 May 2024

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Journal articles on the topic "Automatic speaker recognition"

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Aung, Dr Zaw Win. "Automatic Attendance System Using Speaker Recognition." International Journal of Trend in Scientific Research and Development Volume-2, Issue-6 (October 31, 2018): 802–6. http://dx.doi.org/10.31142/ijtsrd18763.

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Singh, Satyanand. "Forensic and Automatic Speaker Recognition System." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 5 (October 1, 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 a contextual bias or pre-centric understanding of a reference model with the validity of an unknown audio sample and any suspicious individual. Many researchers are continuing to develop automatic algorithms in signal processing and machine learning so that improving performance can effectively introduce the speaker’s identity, where the automatic system performs equally with the human audience. In this paper, I examine the literature about the identification of speakers by machines and humans, emphasizing the key technical speaker pattern emerging for the automatic technology in the last decade. I focus on many aspects of automatic speaker recognition (ASR) systems, including speaker-specific features, speaker models, standard assessment data sets, and performance metrics</span>

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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 (June 30, 2014): e007. http://dx.doi.org/10.3989/loquens.2014.007.

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Algabri, Mohammed, Hassan Mathkour, Mohamed A. Bencherif, Mansour Alsulaiman, and Mohamed A. Mekhtiche. "Automatic Speaker Recognition for Mobile Forensic Applications." Mobile Information Systems 2017 (2017): 1–6. http://dx.doi.org/10.1155/2017/6986391.

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Presently, lawyers, law enforcement agencies, and judges in courts use speech and other biometric features to recognize suspects. In general, speaker recognition is used for discriminating people based on their voices. The process of determining, if a suspected speaker is the source of trace, is called forensic speaker recognition. In such applications, the voice samples are most probably noisy, the recording sessions might mismatch each other, the sessions might not contain sufficient recording for recognition purposes, and the suspect voices are recorded through mobile channel. The identification of a person through his voice within a forensic quality context is challenging. In this paper, we propose a method for forensic speaker recognition for the Arabic language; the King Saud University Arabic Speech Database is used for obtaining experimental results. The advantage of this database is that each speaker’s voice is recorded in both clean and noisy environments, through a microphone and a mobile channel. This diversity facilitates its usage in forensic experimentations. Mel-Frequency Cepstral Coefficients are used for feature extraction and the Gaussian mixture model-universal background model is used for speaker modeling. Our approach has shown low equal error rates (EER), within noisy environments and with very short test samples.

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Besacier, Laurent, and Jean-François Bonastre. "Subband architecture for automatic speaker recognition." Signal Processing 80, no. 7 (July 2000): 1245–59. http://dx.doi.org/10.1016/s0165-1684(00)00033-5.

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FarrÚs, Mireia. "Voice Disguise in Automatic Speaker Recognition." ACM Computing Surveys 51, no. 4 (September 6, 2018): 1–22. http://dx.doi.org/10.1145/3195832.

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Drygajlo, A. "Forensic Automatic Speaker Recognition [Exploratory DSP]." IEEE Signal Processing Magazine 24, no. 2 (March 2007): 132–35. http://dx.doi.org/10.1109/msp.2007.323278.

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Zhang, Cuiling, and Tiejun Tan. "Voice disguise and automatic speaker recognition." Forensic Science International 175, no. 2-3 (March 2008): 118–22. http://dx.doi.org/10.1016/j.forsciint.2007.05.019.

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Singh, Satyanand. "High level speaker specific features modeling in automatic speaker recognition system." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 2 (April 1, 2020): 1859. http://dx.doi.org/10.11591/ijece.v10i2.pp1859-1867.

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Spoken words convey several levels of information. At the primary level, the speech conveys words or spoken messages, but at the secondary level, the speech also reveals information about the speakers. This work is based on the high-level speaker-specific features on statistical speaker modeling techniques that express the characteristic sound of the human voice. Using Hidden Markov model (HMM), Gaussian mixture model (GMM), and Linear Discriminant Analysis (LDA) models build Automatic Speaker Recognition (ASR) system that are computational inexpensive can recognize speakers regardless of what is said. The performance of the ASR system is evaluated for clear speech to a wide range of speech quality using a standard TIMIT speech corpus. The ASR efficiency of HMM, GMM, and LDA based modeling technique are 98.8%, 99.1%, and 98.6% and Equal Error Rate (EER) is 4.5%, 4.4% and 4.55% respectively. The EER improvement of GMM modeling technique based ASR systemcompared with HMM and LDA is 4.25% and 8.51% respectively.

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Khalil, Driss, Amrutha Prasad, Petr Motlicek, Juan Zuluaga-Gomez, Iuliia Nigmatulina, Srikanth Madikeri, and Christof Schuepbach. "An Automatic Speaker Clustering Pipeline for the Air Traffic Communication Domain." Aerospace 10, no. 10 (October 10, 2023): 876. http://dx.doi.org/10.3390/aerospace10100876.

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In air traffic management (ATM), voice communications are critical for ensuring the safe and efficient operation of aircraft. The pertinent voice communications—air traffic controller (ATCo) and pilot—are usually transmitted in a single channel, which poses a challenge when developing automatic systems for air traffic management. Speaker clustering is one of the challenges when applying speech processing algorithms to identify and group the same speaker among different speakers. We propose a pipeline that deploys (i) speech activity detection (SAD) to identify speech segments, (ii) an automatic speech recognition system to generate the text for audio segments, (iii) text-based speaker role classification to detect the role of the speaker—ATCo or pilot in our case—and (iv) unsupervised speaker clustering to create a cluster of each individual pilot speaker from the obtained speech utterances. The speech segments obtained by SAD are input into an automatic speech recognition (ASR) engine to generate the automatic English transcripts. The speaker role classification system takes the transcript as input and uses it to determine whether the speech was from the ATCo or the pilot. As the main goal of this project is to group the speakers in pilot communication, only pilot data acquired from the classification system is employed. We present a method for separating the speech parts of pilots into different clusters based on the speaker’s voice using agglomerative hierarchical clustering (AHC). The performance of the speaker role classification and speaker clustering is evaluated on two publicly available datasets: the ATCO2 corpus and the Linguistic Data Consortium Air Traffic Control Corpus (LDC-ATCC). Since the pilots’ real identities are unknown, the ground truth is generated based on logical hypotheses regarding the creation of each dataset, timing information, and the information extracted from associated callsigns. In the case of speaker clustering, the proposed algorithm achieves an accuracy of 70% on the LDC-ATCC dataset and 50% on the more noisy ATCO2 dataset.

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Dissertations / Theses on the topic "Automatic speaker recognition"

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Deterding, David Henry. "Speaker normalisation for automatic speech recognition." Thesis, University of Cambridge, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.359822.

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Vogt, Robert Jeffery. "Automatic speaker recognition under adverse conditions." Thesis, Queensland University of Technology, 2006. https://eprints.qut.edu.au/36195/1/Robert_Vogt_Thesis.pdf.

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Speaker verification is the process of verifying the identity of a person by analysing their speech. There are several important applications for automatic speaker verification (ASV) technology including suspect identification, tracking terrorists and detecting a person’s presence at a remote location in the surveillance domain, as well as person authentication for phone banking and credit card transactions in the private sector. Telephones and telephony networks provide a natural medium for these applications. The aim of this work is to improve the usefulness of ASV technology for practical applications in the presence of adverse conditions. In a telephony environment, background noise, handset mismatch, channel distortions, room acoustics and restrictions on the available testing and training data are common sources of errors for ASV systems. Two research themes were pursued to overcome these adverse conditions: Modelling mismatch and modelling uncertainty. To directly address the performance degradation incurred through mismatched conditions it was proposed to directly model this mismatch. Feature mapping was evaluated for combating handset mismatch and was extended through the use of a blind clustering algorithm to remove the need for accurate handset labels for the training data. Mismatch modelling was then generalised by explicitly modelling the session conditions as a constrained offset of the speaker model means. This session variability modelling approach enabled the modelling of arbitrary sources of mismatch, including handset type, and halved the error rates in many cases. Methods to model the uncertainty in speaker model estimates and verification scores were developed to address the difficulties of limited training and testing data. The Bayes factor was introduced to account for the uncertainty of the speaker model estimates in testing by applying Bayesian theory to the verification criterion, with improved performance in matched conditions. Modelling the uncertainty in the verification score itself met with significant success. Estimating a confidence interval for the "true" verification score enabled an order of magnitude reduction in the average quantity of speech required to make a confident verification decision based on a threshold. The confidence measures developed in this work may also have significant applications for forensic speaker verification tasks.

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Zhang, Xiaozheng. "Automatic speechreading for improved speech recognition and speaker verification." Diss., Georgia Institute of Technology, 2002. http://hdl.handle.net/1853/13067.

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Ho, Ka-Lung. "Kernel eigenvoice speaker adaptation /." View Abstract or Full-Text, 2003. http://library.ust.hk/cgi/db/thesis.pl?COMP%202003%20HOK.

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Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2003.
Includes bibliographical references (leaves 56-61). Also available in electronic version. Access restricted to campus users.

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Thiruvaran, Tharmarajah Electrical Engineering &amp Telecommunications Faculty of Engineering UNSW. "Automatic speaker recognition using phase based features." Awarded by:University of New South Wales. Electrical Engineering & Telecommunications, 2009. http://handle.unsw.edu.au/1959.4/44705.

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Despite recent advances, improving the accuracy of automatic speaker recognition systems remains an important and challenging area of research. This thesis investigates two-phase based features, namely the frequency modulation (FM) feature and the group delay feature in order to improve the speaker recognition accuracy. Introducing features complementary to spectral envelope-based features is a promising approach for increasing the information content of the speaker recognition system. Although phase-based features are motivated by psychophysics and speech production considerations, they have rarely been incorporated into speaker recognition front-ends. A theory has been developed and reported in this thesis, to show that the FM component can be extracted using second-order all pole modelling, and a technique for extracting FM features using this model is proposed, to produce very smooth, slowly varying FM features that are effective for speaker recognition tasks. This approach is shown herein to significantly improve speaker recognition performance over other existing FM extraction methods. A highly computationally efficient FM estimation technique is then proposed and its computational efficiency is shown through a comparative study with other methods with respect to the trade off between computational complexity and performance. In order to further enhance the FM based front-end specifically for speaker recognition, optimum frequency band allocation is studied in terms of the number of sub-bands and spacing of centre frequencies, and two new frequency band re-allocations are proposed for FM based speaker recognition. Two group delay features are also proposed: log compressed group delay feature and the sub-band group delay feature, to address problems in group delay caused by the zeros of the z-transform polynomial of a speech signal being close to the unit circle. It has been shown that the combination of group delay and FM, complements Mel Frequency Cepstral Coefficient (MFCC) in speaker recognition tasks. Furthermore, the proposed FM feature is successfully utilised for automatic forensic speaker recognition, which is implemented based on the likelihood ratio framework with two stage modelling and calibration, and shown to behave in a complementary manner to MFCCs. Notably, the FM based system provides better calibration loss than the MFCC based system, suggesting less ambiguity of FM information than MFCC information in an automatic forensic speaker recognition system. In order to demonstrate the effectiveness of FM features in a large scale speaker recognition environment, an FM-based speaker recognition subsystem is developed and submitted to the NIST 2008 speaker recognition evaluation as part of the I4U submission. Post evaluation analysis shows a 19.7% relative improvement over the traditional MFCC based subsystem when it is augmented by the FM based subsystem. Consistent improvements in performance are obtained when MFCC is augmented with FM in all sub-categories of NIST 2008, in three development tasks and for the NIST 2001 database, demonstrating the complementary behaviour of MFCC and FM features.

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Chan, Carlos Chun Ming. "Speaker model adaptation in automatic speech recognition." Thesis, Robert Gordon University, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.339307.

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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 actions from the user, such as indicating patterns of the speech signal and noise. The system of the features was proposed. This system consists of parameters of excitation source (glottal) and parameters of the vocal tract. The fundamental frequency was taken as an excitation source parameter and four formants with three antiformants were taken as parameters of the vocal tract. In order to equate dispersions of the formants and antiformants we propose to use them in mel-frequency scale. The standard mel-frequency cepstral coefficients (MFCC) for comparison of the results were implemented in the recognition system too. These features make baseline in speech and speaker recognition. The experiments of speaker recognition have shown that our proposed system of features outperformed standard mel-frequency cepstral coefficients. The equal error rate (EER) was equal to 5.17% using proposed... [to full text]
Disertacijoje nagrinėjami kalbančiojo atpažinimo pagal balsą klausimai. Aptartos kalbančiojo atpažinimo sistemos, jų raida, atpažinimo problemos, požymių sistemos įvairovė bei kalbančiojo modeliavimo ir požymių palyginimo metodai, naudojami nuo ištarto teksto nepriklausomame bei priklausomame kalbančiojo atpažinime. Darbo metu sukurta nuo ištarto teksto nepriklausanti kalbančiojo atpažinimo sistema. Kalbėtojų modelių kūrimui ir požymių palyginimui buvo panaudoti Gauso mišinių modeliai. Pasiūlytas automatinis vokalizuotų garsų išrinkimo (segmentavimo) metodas. Šis metodas yra greitai veikiantis ir nereikalaujantis iš vartotojo jokių papildomų veiksmų, tokių kaip kalbos signalo ir triukšmo pavyzdžių nurodymas. Pasiūlyta požymių vektorių sistema, susidedanti iš žadinimo signalo bei balso trakto parametrų. Kaip žadinimo signalo parametras, panaudotas žadinimo signalo pagrindinis dažnis, kaip balso trakto parametrai, panaudotos keturios formantės bei trys antiformantės. Siekiant suvienodinti žemesnių bei aukštesnių formančių ir antiformančių dispersijas, jas pasiūlėme skaičiuoti melų skalėje. Rezultatų palyginimui sistemoje buvo realizuoti standartiniai požymiai, naudojami kalbos bei asmens atpažinime – melų skalės kepstro koeficientai (MSKK). Atlikti kalbančiojo atpažinimo eksperimentai parodė, kad panaudojus pasiūlytą požymių sistemą buvo gauti geresni atpažinimo rezultatai, nei panaudojus standartinius požymius (MSKK). Gautas lygių klaidų lygis, panaudojant pasiūlytą požymių... [toliau žr. visą tekstą]

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Chan, Chit-man. "Speaker-independent recognition of Putonghua finals /." [Hong Kong : University of Hong Kong], 1987. http://sunzi.lib.hku.hk/hkuto/record.jsp?B12363091.

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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.
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 feature sets are investigated. During April 2004, the University of Stellenbosch, in collaboration with Spescom DataVoice, participated in an international speaker verification competition presented by the National Institute of Standards and Technology (NIST). The University of Stellenbosch submitted phonetic and lexical (non-acoustic) speaker recognition systems and a fused system (the primary system) that fuses the acoustic system of Spescom DataVoice with the non-acoustic systems of the University of Stellenbosch. The results were evaluated by means of a cost model. Based on the cost model, the primary system obtained second and third position in the two categories that were submitted.
AFRIKAANSE OPSOMMING: Hierdie projek maak gebruik van foneem-etikette wat geklassifiseer word deur ’n foneemherkenner en daarna gebruik word vir fonetiese sprekerherkenning. Die tyd-afhanklikhede tussen foneme word gemodelleer deur gebruik te maak van verskuilde Markov modelle (HMMs) as sprekermodelle. Daar word ge¨eksperimenteer met eerste-orde en tweede-orde HMMs en verskeie vergladdingstegnieke word ondersoek om dataskaarsheid aan te spreek. Die gebruik van woord-etikette vir sprekerherkenning word ook ondersoek. Enkelwoordfrekwensies word getel en daar word ge¨eksperimenteer met verskeie woordseleksies as kenmerke vir sprekerherkenning. Gedurende April 2004 het die Universiteit van Stellenbosch in samewerking met Spescom DataVoice deelgeneem aan ’n internasionale sprekerverifikasie kompetisie wat deur die National Institute of Standards and Technology (NIST) aangebied is. Die Universiteit van Stellenbosch het ingeskryf vir ’n fonetiese en ’n woordgebaseerde (nie-akoestiese) sprekerherkenningstelsel, asook ’n saamgesmelte stelsel wat as primˆere stelsel dien. Die saamgesmelte stelsel is ’n kombinasie van Spescom DataVoice se akoestiese stelsel en die twee nie-akoestiese stelsels van die Universiteit van Stellenbosch. Die resultate is ge¨evalueer deur gebruik te maak van ’n koste-model. Op grond van die koste-model het die primˆere stelsel tweede en derde plek behaal in die twee kategorie¨e waaraan deelgeneem is.

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Chan, Chit-man, and 陳哲民. "Speaker-independent recognition of Putonghua finals." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1987. http://hub.hku.hk/bib/B12363091.

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(Uncorrected OCR) Abstract of thesis entitled Speaker- Independent Recognition of Putonghua Finals submitted by CHAN, Chit Man for the degree of Doctor of Philosophy at the University of Hong Kong � In December 1987 ABSTRACT A detailed study had been performed to address the problem of speaker-independent recognition of Putonghua (Mandarin) finals. The study included 35 Putonghua finals, 16 of which having trailing nasals. They were spoken by 51 speakers: 38 females, 13 males, in 5 different tones for two times. The sample was spectrally analyzed by a bank of 18 nonoverlapping critical-band filters. Three data reduction techniques: Karhunen-Loeve Transformation (KLT) , Discrete Cosine Transformation (OCT) and Stepwise Discriminant Analysis (SDA) , were comparat i vely studied for their feature representation capability. The results indicated that KLT was superior to both OCT and SDA. Furthermore, the theoretic equivalence of OCT to KLT was found to be valid only with 5 or more feature dimensions used in computation. On the other hand, the results also showed that the Hahalanobis and a proposed modified Mahalanobis distance both gave a better measurement of performance than the other distances tested, which included the City Block, Euclidean, Minkowski, and Chebyshev. .,. In the second Part of the study, the Hidden Markov Modelling (HMM) technique was investigated. Three classification methods: Phonemic Labell ing (PL), Vector Quantization (VQ) and a proposed Hybrid Symbol (HS) generation, were studied for use with HMM. Whilst PL was found to be simple and efficient, its performance was not as good as VQ. However, the time taken by VQ was excessive, especially in training. The results with the HS method showed that it .could successfully merge the speed advantage of PL and the better discriminatory power of VQ. An approximately 80% saving in the quantizer training time could be achieved with only a marginal loss in performance. At the same time, it Abs-l Abstract was also found that allowing skipping of states in a Left-to-Right model (LRM) could lead to a negative effect on overall recognition. As an indication of performance, the recognition rate of the simulated system was 81.3%, 95.0% and 98.0% with the best I, 2, and 3 candidates included, respectively, using a 256-level VQ and a 6-state, no-skip LRM on a sample of 8,400 finals from 48 speakers. The specific rates on non-nasal finals achieved even 96% - 98% using the best candidate alone . .. ," Abs-2
abstract
toc
Electrical and Electronic Engineering
Doctoral
Doctor of Philosophy

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Books on the topic "Automatic speaker recognition"

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Lee, Chin-Hui, Frank K. Soong, and Kuldip K. Paliwal, eds. Automatic Speech and Speaker Recognition. Boston, MA: 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. Chichester, UK: John Wiley & Sons, Ltd, 2009. http://dx.doi.org/10.1002/9780470742044.

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Fundamentals of speaker recognition. New York: Springer, 2011.

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

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

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service), SpringerLink (Online, ed. Information Security for Automatic Speaker Identification. New York, NY: Springer Science+Business Media, LLC, 2011.

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Fernández Gallardo, Laura. Human and Automatic Speaker Recognition over Telecommunication Channels. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-287-727-7.

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Speaker separation and tracking. Konstanz: Hartung-Gorre Verlag, 2006.

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Joseph, Keshet, and Bengio Samy, eds. Automatic speech and speaker recognition: Large margin and kernel methods. Hoboken, NJ: J. Wiley & Sons, 2009.

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Russell, M. J. The development of the speaker independent ARM continuous speech recognition system. [London: Controller, H.M.S.O., 1992.

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Book chapters on the topic "Automatic speaker recognition"

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Ghate, P. M., Shraddha Chadha, Aparna Sundar, and Ankita Kambale. "Automatic Speaker Recognition System." In Advances in Intelligent Systems and Computing, 1037–44. New Delhi: Springer India, 2013. http://dx.doi.org/10.1007/978-81-322-0740-5_126.

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Ramasubramanian, V. "Speaker Spotting: Automatic Telephony Surveillance for Homeland Security." In Forensic Speaker Recognition, 427–68. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-0263-3_15.

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Drygajlo, Andrzej. "Automatic Speaker Recognition for Forensic Case Assessment and Interpretation." In Forensic Speaker Recognition, 21–39. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-0263-3_2.

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Eriksson, Anders. "Aural/Acoustic vs. Automatic Methods in Forensic Phonetic Case Work." In Forensic Speaker Recognition, 41–69. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-0263-3_3.

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Watt, Dominic, and Georgina Brown. "Forensic phonetics and automatic speaker recognition." In The Routledge Handbook of Forensic Linguistics, 400–415. Title: The Routledge handbook of forensic linguistics / edited by Malcolm Coulthard, Alison May, Rui Sousa-Silva. Description: Second edition. | London ; New York : Routledge, 2020. | Series: Routledge handbooks in applied linguistics: Routledge, 2020. http://dx.doi.org/10.4324/9780429030581-32.

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Fernández Gallardo, Laura. "Detecting Speaker-Discriminative Spectral Content in Wideband for Automatic Speaker Recognition." In Human and Automatic Speaker Recognition over Telecommunication Channels, 85–112. Singapore: Springer Singapore, 2015. http://dx.doi.org/10.1007/978-981-287-727-7_6.

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Fernández Gallardo, Laura. "Relations Among Speech Quality, Human Speaker Identification, and Automatic Speaker Verification." In Human and Automatic Speaker Recognition over Telecommunication Channels, 113–43. Singapore: Springer Singapore, 2015. http://dx.doi.org/10.1007/978-981-287-727-7_7.

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Rosique–López, Lina, and Vicente Garcerán–Hernández. "Bio-inspired System in Automatic Speaker Recognition." In New Challenges on Bioinspired Applications, 315–23. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21326-7_34.

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Saquib, Zia, Nirmala Salam, Rekha P. Nair, Nipun Pandey, and Akanksha Joshi. "A Survey on Automatic Speaker Recognition Systems." In Communications in Computer and Information Science, 134–45. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17641-8_18.

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Drygajlo, Andrzej, and Rudolf Haraksim. "Biometric Evidence in Forensic Automatic Speaker Recognition." In Handbook of Biometrics for Forensic Science, 221–39. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-50673-9_10.

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Conference papers on the topic "Automatic speaker recognition"

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Badji, Aliou, Youssou Dieng, Ibrahima Diop, Papa Alioune Cisse, and Boubacar Diouf. "Automatic Speaker Recognition (ASR)." In ICIST '20: 10th International Conference on Information Systems and Technologies. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3447568.3448544.

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Reynolds, Doug, and Marc Zissman. "Automatic speaker and language recognition." In the 2003 Conference of the North American Chapter of the Association for Computational Linguistics. Morristown, NJ, USA: Association for Computational Linguistics, 2003. http://dx.doi.org/10.3115/1075168.1075177.

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Chao, Guan-Lin, John Paul Shen, and Ian Lane. "Deep Speaker Embedding for Speaker-Targeted Automatic Speech Recognition." In NLPIR 2019: 2019 the 3rd International Conference on Natural Language Processing and Information Retrieval. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3342827.3342847.

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Malik, S., and Fayyaz A. Afsar. "Wavelet transform based automatic speaker recognition." In 2009 IEEE 13th International Multitopic Conference (INMIC). IEEE, 2009. http://dx.doi.org/10.1109/inmic.2009.5383083.

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Kurian, Betty, V. R. Sreehari, and Leena Mary. "PNCC for forensic automatic speaker recognition." In PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON MICROELECTRONICS, SIGNALS AND SYSTEMS 2019. AIP Publishing, 2020. http://dx.doi.org/10.1063/5.0003967.

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Li, Ruirui, Jyun-Yu Jiang, Jiahao Liu Li, Chu-Cheng Hsieh, and Wei Wang. "Automatic Speaker Recognition with Limited Data." In WSDM '20: The Thirteenth ACM International Conference on Web Search and Data Mining. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3336191.3371802.

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Bonastre, Jean-Francois, and Henri Meloni. "Automatic speaker recognition and analytic process." In 3rd European Conference on Speech Communication and Technology (Eurospeech 1993). ISCA: ISCA, 1993. http://dx.doi.org/10.21437/eurospeech.1993-123.

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Chettri, Bhusan, Tomi Kinnunen, and Emmanouil Benetos. "Subband Modeling for Spoofing Detection in Automatic Speaker Verification." In Odyssey 2020 The Speaker and Language Recognition Workshop. ISCA: ISCA, 2020. http://dx.doi.org/10.21437/odyssey.2020-48.

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Brown, Georgina. "Segmental Content Effects on Text-dependent Automatic Accent Recognition." In Odyssey 2018 The Speaker and Language Recognition Workshop. ISCA: ISCA, 2018. http://dx.doi.org/10.21437/odyssey.2018-2.

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Kral, Pavel. "Discrete Wavelet Transform for automatic speaker recognition." In 2010 3rd International Congress on Image and Signal Processing (CISP). IEEE, 2010. http://dx.doi.org/10.1109/cisp.2010.5646691.

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Reports on the topic "Automatic speaker recognition"

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Oran, D. Requirements for Distributed Control of Automatic Speech Recognition (ASR), Speaker Identification/Speaker Verification (SI/SV), and Text-to-Speech (TTS) Resources. RFC Editor, December 2005. http://dx.doi.org/10.17487/rfc4313.

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Issues in Data Processing and Relevant Population Selection. OSAC Speaker Recognition Subcommittee, November 2022. http://dx.doi.org/10.29325/osac.tg.0006.

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Abstract:

In Forensic Automatic Speaker Recognition (FASR), forensic examiners typically compare audio recordings of a speaker whose identity is in question with recordings of known speakers to assist investigators and triers of fact in a legal proceeding. The performance of automated speaker recognition (SR) systems used for this purpose depends largely on the characteristics of the speech samples being compared. Examiners must understand the requirements of specific systems in use as well as the audio characteristics that impact system performance. Mismatch conditions between the known and questioned data samples are of particular importance, but the need for, and impact of, audio pre-processing must also be understood. The data selected for use in a relevant population can also be critical to the performance of the system. This document describes issues that arise in the processing of case data and in the selections of a relevant population for purposes of conducting an examination using a human supervised automatic speaker recognition approach in a forensic context. The document is intended to comply with the Organization of Scientific Area Committees (OSAC) for Forensic Science Technical Guidance Document.

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