Dissertations / Theses on the topic 'Speaker identification systems'
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Wildermoth, Brett Richard, and n/a. "Text-Independent Speaker Recognition Using Source Based Features." Griffith University. School of Microelectronic Engineering, 2001. http://www4.gu.edu.au:8080/adt-root/public/adt-QGU20040831.115646.
Full textWildermoth, Brett Richard. "Text-Independent Speaker Recognition Using Source Based Features." Thesis, Griffith University, 2001. http://hdl.handle.net/10072/366289.
Full textThesis (Masters)
Master of Philosophy (MPhil)
School of Microelectronic Engineering
Faculty of Engineering and Information Technology
Full Text
Phythian, Mark. "Speaker identification for forensic applications." Thesis, Queensland University of Technology, 1998. https://eprints.qut.edu.au/36079/3/__qut.edu.au_Documents_StaffHome_StaffGroupR%24_rogersjm_Desktop_36079_Digitised%20Thesis.pdf.
Full textGedik, Berk. "Evaluation of Text-Independent and Closed-Set Speaker Identification Systems." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-239625.
Full textTalarigenkänning är en benämning på tekniker som syftar till att identifiera en talare givet en inspelning av dennes röst; dessa tekniker har ett brett användningsområde. I det här examensarbetet tillämpas ett antal maskininlärningsmodeller på uppgiften att känna igen talare. Modellerna är Gaussian Mixture Model(GMM), k-Nearest Neighbour(k-NN) och Support Vector Machine(SVM). Olika tekniker för att ta fram variabler till modelleringen provas, såsom Mel-Frequency Cepstral Coefficients (MFCC) och Linear Predictive Cepstral Coefficients (LPCC). Teknikernas lämplighet för talarigenkänning undersöks. Kombinationer av ovan nämnda modeller och tekniker utvärderas över många olika dataset som skiljer sig åt i antalet talare samt mängden data. På så sätt utvärderas och analyseras de olika metoderna för olika förut- sättningar. Resultaten innehåller bland annat utfallen att både GMM och kNN ger hög träffsäkerhet medan LPCC ger högre träffsäkerhet än MFCC. Även effekten av inspelningslängden för de olika rösterna, den sammanlagda längden på träningsdatan samt antalet talare på de olika modellerna analyseras och presenteras.
Cohen, Zachary Gideon. "Noise Reduction with Microphone Arrays for Speaker Identification." DigitalCommons@CalPoly, 2012. https://digitalcommons.calpoly.edu/theses/884.
Full textChan, Siu Man. "Improved speaker verification with discrimination power weighting /." View abstract or full-text, 2004. http://library.ust.hk/cgi/db/thesis.pl?ELEC%202004%20CHANS.
Full textIncludes bibliographical references (leaves 86-93). Also available in electronic version. Access restricted to campus users.
Al-Kaltakchi, Musab Tahseen Salahaldeen. "Robust text independent closed set speaker identification systems and their evaluation." Thesis, University of Newcastle upon Tyne, 2018. http://hdl.handle.net/10443/3978.
Full textReynolds, Douglas A. "A Gaussian mixture modeling approach to text-independent speaker identification." Diss., Georgia Institute of Technology, 1992. http://hdl.handle.net/1853/16903.
Full textLeis, John W. "Spectral coding methods for speech compression and speaker identification." Thesis, Queensland University of Technology, 1998. https://eprints.qut.edu.au/36062/7/36062_Digitised_Thesis.pdf.
Full textWark, Timothy J. "Multi-modal speech processing for automatic speaker recognition." Thesis, Queensland University of Technology, 2001.
Find full textCastellano, Pierre John. "Speaker recognition modelling with artificial neural networks." Thesis, Queensland University of Technology, 1997.
Find full textSlomka, Stefan. "Multiple classifier structures for automatic speaker recognition under adverse conditions." Thesis, Queensland University of Technology, 1999.
Find full textBarger, Peter James. "Speech processing for forensic applications." Thesis, Queensland University of Technology, 1998. https://eprints.qut.edu.au/36081/1/36081_Barger_1998.pdf.
Full textLucey, Simon. "Audio-visual speech processing." Thesis, Queensland University of Technology, 2002. https://eprints.qut.edu.au/36172/7/SimonLuceyPhDThesis.pdf.
Full textHubeika, Valiantsina. "Intersession Variability Compensation in Language and Speaker Identification." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2008. http://www.nusl.cz/ntk/nusl-235432.
Full textAl-Ani, Ahmed Karim. "An improved pattern classification system using optimal feature selection, classifier combination, and subspace mapping techniques." Thesis, Queensland University of Technology, 2002.
Find full textHuang, Hung-Pin, and 黃紘斌. "Robust Speaker Identification Systems against Additive Noise and Reverberation." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/82731413646820206152.
Full text國立交通大學
電信工程研究所
100
Conventional speaker recognition systems usually use MFCCs as features, which are known as low-level features and the performance are severely compromised by interference, such as additive or convolutional noises. On the contrary, the high-level features seemed to be less accuracy in clean condition but more robustness in noisy environment. In this thesis, we first pass the speech wave thorough the hearing models, then choose 9 sets of outputs after spectro-temporal modulation filtering as our features and apply those features for robust speaker recognition. The results show that, in reverberant condition, we performs better after T60 is more than 0.6, and in noisy criteria, we get significant improvement for all SNR conditions compare to MFCCs ,and superior performance to ANTCCs is also showed in low SNR conditions.
Mohan, Aanchan K. "Combining speech recognition and speaker verification." 2008. http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.17528.
Full textLin, Guan-Liang, and 林冠良. "An MFCC-based Speaker Identification System." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/fx7824.
Full text東海大學
資訊工程學系
105
Nowadays, speech recognition has many practical applications which are currently used by people in the world. Typical examples are the SIRI of iPhone, Google speech recognition system, and mobile phones operated by voice, etc. On the contrary, speaker identification in its current stage is relatively immature. Therefore, in this paper, we study a speaker identification technique which first takes the original voice signals of a person, e.g., Bob. After that, the voice signals is converted from time domain to frequency domain by employing the Fourier transformation approach. A MFCC-based human auditory filtering model is then utilized to adjust the energy levels of different frequencies as the quantified characteristics of Bob’s voice. Next, the energies are normalized to the scales of logarithm as the feature of the voice signals. Further, the probability density function of Gaussian mixture model is employed to represent the distribution of the logarithmic characteristics as Bob’s specific acoustic model. When receiving an unknown person, e.g., x’s voice, the system processes the voice with the same procedure, and compares the processing result, which is x’s acoustic model, with known-people’s acoustic models collected in an acoustic-model database beforehand to identify who the most possible speaker is.
Lyu, Yi-Chen, and 呂易宸. "Speech Access System based on Speaker Identification." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/49174049479521734264.
Full text國立中央大學
電機工程研究所
99
The purpose of this thesis is to design a speech access system with speaker recognition technology which can determine whether the input sound of the user voice is valid or not. Combined with keywords spotting technology, the system can identify the name of users. And coupled with text-to-speech technology, the system uses not only a text but also human voice response. System built by Microsoft Foundation Classes (MFC) windows based interface is facilitated for the user to operate. Because access control system needs to meet the requirements of real-time or online, as the result, the consumed time of used methods must take into account because users would not spend much time waiting for results. Therefore, methods must be selective since they affect the recognition rate and time seems to be regarded as the prerequisite element while selecting the appropriate algorithm. There are 40 participants join this test, and there are 38 target users among them, while the other two are imposers. Speaker recognition rate is 94.9%, the false acceptance rate is 0.8%, and the keyword recognition rate is 90.6%. The average recognition sentences are about 0.5 seconds each. Identification has been up to the real-time requirements.
Yu, Ming-Der, and 于明德. "A Design of Text Independent Speaker Identification System for Telephone Speech." Thesis, 1997. http://ndltd.ncl.edu.tw/handle/48072274198405347870.
Full text國立中山大學
電機工程研究所
85
A Text independent speaker identification system based on long-term spectral feature averaging (LTA) and Karhunen-Loeve transform (KLT) for telephone speech is proposed. This system uses basis function derived from KLT to effectively reduce the data volume and preserve most of the identification information for each speaker. A database with 61 male and 72 female mandarin speakers recorded from the telephone answering system are collected for system evaluation. By the use of the first 28 features from the 128 basis functions, it is demonstrated that the correct classification rate can reach 88% if our special frame selection criterion is used, and correct classification rate is 73% only if the special frame selection criterion is not used! And the same time the classification time can be reduced to about 3% of the classification time with 128 features!
CHIANG, CHANG YEN, and 蔣昌言. "Text-Independent Speaker Identification System Based on Karhunen-Loeve Transform and Quadratic Classifier." Thesis, 1996. http://ndltd.ncl.edu.tw/handle/19284077261651106741.
Full textTsai, Zong-Syuan, and 蔡宗軒. "Speaker Identification System based on Long Term Average Spectrum and Speech Content Distribution." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/10322757746063947883.
Full text國立臺灣大學
電機工程學研究所
100
Timbre is the important characteristic that human can distinguish the difference between each other by their voice. This thesis aims to give a feature of timbre that considers both Long Term Average Spectrum (LTAS) and speech content distribution and implements to speaker identification system. LTAS is a feature influenced by both characteristics of speaker and content, so the same speaker still has inconsistent patterns of LTAS. The inconsistency of patterns also directly influences the accuracy of speaker identification using LTAS as feature. To increase the accuracy by considering the effect of content, this thesis proposes the idea of Pseudo LTAS. All Taiwanese Mandarin phonemes are analyzed. Then the influential phonemes are chosen and their average spectra are derived as the components of speaker database. When the test speech signal is inputted, system recognizes its content and synthesizes the pseudo LTAS weighted by the content for individual. Because the contents of Pseudo LTAS and test speech signal are same, the accuracy of speaker identification using Pseudo LTAS as the decision pattern will be better than the one using LTAS which ignores the influence of content. The accuracy of speaker identification system using Pseudo LTAS is 94.2 %.
Sanchez, Jose Boris Meyer-Baese Anke. "Speaker identification based on an integrated system combining cepstral feature extraction and vector quantization." Diss., 2005. http://etd.lib.fsu.edu/theses/available/etd-04172005-151234.
Full textAdvisor: Dr. Anke Meyer-Baese, Florida State University, College of Engineering, Dept. of Electrical Engineering. Title and description from dissertation home page (viewed June 15, 2005). Document formatted into pages; contains vii, 30 pages. Includes bibliographical references.
古詩峰. "A Large Population Speaker Identification System Based on Wavelet Transform Features by Using Microphone and Telephone Speech Corpus." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/54547071414963915299.
Full text長庚大學
電機工程研究所
91
In this thesis, we have established a large population speaker identification system which is based on Gaussian Mixture Models using wavelet based features. The corpus for experiments of this thesis are the well-known TIMIT phonetically balance speech database for microphone speech (TIMIT) and a new collected Formosa Speech Corpus for telephone speech (FSC-Tel 1000). We achieve the best speaker accuracy of 99.127% for TIMIT corpus and 95.5% for FSC-Tel 1000 corpus. About wavelet based features, our objective is to found a suitable wavelet decomposing tree by which the speech waveform could be transformed to the wavelet coefficients. In our experiments, we found that different wavelet decomposing trees play an important role for the accuracy of a speaker identification system. In the thesis we have found several efficient types of wavelet decomposing trees. However the optimal trees have not yet been found. We also implemented a speaker identification system on Windows 2000/XP in which a user only speaks a segment of speech of about 25 seconds for registering his voice in the system. In real-time testing, each speaker speaks only 2 seconds and then the system can correctly identify him with a high accuracy. In the system the feature extraction consumes the most time. For this reason we must found a good algorithm to speed up the feature extraction.