Relevant bibliographies by topics / Speaker identification

Contents

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

Academic literature on the topic 'Speaker identification'

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

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Journal articles on the topic "Speaker identification"

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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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Dissertations / Theses on the topic "Speaker identification"

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Fredrickson, Steven Eric. "Neural networks for speaker identification." Thesis, University of Oxford, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.294364.

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Zhao, Xiaojia. "CASA-BASED ROBUST SPEAKER IDENTIFICATION." The Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1402620178.

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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.

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A major application of Speaker Identification (SI) is suspect identification by voice. This thesis investigates techniques that can be used to improve SI technology as applied to suspect identification. Speech Coding techniques have become integrated into many of our modern voice communications systems. This prompts the question - how are automatic speaker identification systems and modern forensic identification techniques affected by the introduction of digitally coded speech channels? Presented in this thesis are three separate studies investigating the effects of speech coding and compres
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Mudry, Andrew H. "Speaker identification using the wavelet transform." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp04/mq22123.pdf.

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Hassan, S. M. "Source feature analysis for speaker identification." Connect to online resource, 2007. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:1442902.

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Hassan, Tariq. "Instantaneous modulation components for speaker identification." Thesis, University of Southampton, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.580590.

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In speaker recognition, we seek to find highly speaker-related features in the speech signal that can be used as the basis of effective classification. The choice of such features should follow the basic standard of speech signal characterisation, namely, to select and model the features that minimise intra-speaker variability and, at the same time, maximise inter-speaker variability. The instantaneous features of the speech signal, which are representing the approximate formant frequencies, its bandwidth, and its carrying energies at each time instant, could reflect, to some extent, the promi
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Jankowski, Charles Robert. "Fine structure features for speaker identification." Thesis, Massachusetts Institute of Technology, 1996. http://hdl.handle.net/1721.1/11012.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1996.<br>Includes bibliographical references (p. 193-198).<br>by Charles Robert Jankowski, Jr.<br>Ph.D.
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Mudry, Andrew H. (Andrew Henry) Carleton University Dissertation Engineering Electronics. "Speaker identification using the wavelet transform." Ottawa, 1997.

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Gudnason, Jon. "Voice source cepstrum processing for speaker identification." Thesis, Imperial College London, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.439448.

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Elder, Andrew George. "Evaluation of glottal characteristics for speaker identification." Thesis, University of Canterbury. Electrical and Electronic Engineering, 1992. http://hdl.handle.net/10092/5831.

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Based on the assumption that the physical characteristics of people's vocal apparatus cause their voices to have distinctive characteristics, this thesis reports on investigations into the use of the long-term average glottal response for speaker identification. The long-term average glottal response is a new feature that is obtained by overlaying successive vocal tract responses within an utterance. The way in which the long-term average glottal response varies with accent and gender is examined using a population of 352 American English speakers from eight different accent regions. Descript
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Books on the topic "Speaker identification"

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Herbig, Tobias, Franz Gerl, and Wolfgang Minker. Self-Learning Speaker Identification. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19899-1.

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El-Samie, Fathi E. Abd. Information Security for Automatic Speaker Identification. Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-9698-5.

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

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Braun, Almut. The Speaker Identification Ability of Blind and Sighted Listeners. Springer Fachmedien Wiesbaden, 2016. http://dx.doi.org/10.1007/978-3-658-15198-0.

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Franz, Gerl, Minker Wolfgang, and SpringerLink (Online service), eds. Self-Learning Speaker Identification: A System for Enhanced Speech Recognition. Springer-Verlag Berlin Heidelberg, 2011.

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Wu, Jin Chu. Data dependency on measurement uncertainties in speaker recognition evaluation. U.S. Dept. of Commerce, National Institute of Standards and Technology, 2011.

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Alderman, Tony Brian. Forensic speaker identification: A likelihood ratio-based approach using vowel formants. LINCOM Europa, 2005.

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Sabourin, Conrad. Computational speech processing: Speech analysis, recognition, understanding, compression, transmission, coding, synthesis, text to speech systems, speech to tactile displays, speaker identification, prosody processing : bibliography. Infolingua, 1994.

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United States. Bureau of Justice Assistance and Ohio. Office of Criminal Justice Services (1993- ), eds. I speak--: Language identification guide. 2nd ed. U.S. Dept. of Justice, Office of Justice Programs, Bureau of Justice Assistance, 2006.

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Nicolas-Séïde, Lucienne. Identification et classification des erreurs en français écrit des étudiants allophones au collégial. Collège de Bois-de-Boulogne, Centre des ressources didactiques et pédagogiques, 1992.

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Book chapters on the topic "Speaker identification"

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Farouk, Mohamed Hesham. "Speaker Identification." In SpringerBriefs in Electrical and Computer Engineering. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-69002-5_8.

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de Jong-Lendle, Gea. "Speaker Identification." In Language as Evidence. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-84330-4_9.

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Braun, Almut. "Speaker identification experiment." In The Speaker Identification Ability of Blind and Sighted Listeners. Springer Fachmedien Wiesbaden, 2016. http://dx.doi.org/10.1007/978-3-658-15198-0_6.

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Herbig, Tobias, Franz Gerl, and Wolfgang Minker. "Combined Speaker Adaptation." In Self-Learning Speaker Identification. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19899-1_4.

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Thakker, Manthan, Shivangi Vyas, Prachi Ved, and S. Shanthi Therese. "Speaker Identification in a Multi-speaker Environment." In Information and Communication Technology for Sustainable Development. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3920-1_24.

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Li, Ying, and C. C. Jay Kuo. "Speaker Identification for Movies." In Video Content Analysis Using Multimodal Information. Springer US, 2003. http://dx.doi.org/10.1007/978-1-4757-3712-7_5.

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Julia, Luc E., Larry P. Heck, and Adam J. Cheyer. "A speaker identification agent." In Audio- and Video-based Biometric Person Authentication. Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/bfb0016003.

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Herbig, Tobias, Franz Gerl, and Wolfgang Minker. "Introduction." In Self-Learning Speaker Identification. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19899-1_1.

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Herbig, Tobias, Franz Gerl, and Wolfgang Minker. "Fundamentals." In Self-Learning Speaker Identification. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19899-1_2.

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Herbig, Tobias, Franz Gerl, and Wolfgang Minker. "Combining Self-Learning Speaker Identification and Speech Recognition." In Self-Learning Speaker Identification. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19899-1_3.

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Conference papers on the topic "Speaker identification"

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Maqueda, Emmanuel, Javier Alvarez, and Ivan Meza. "Towards forensic speaker identification in Spanish using triplet loss." In LatinX in AI at Neural Information Processing Systems Conference 2020. Journal of LatinX in AI Research, 2020. http://dx.doi.org/10.52591/lxai2020121210.

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This work explores the use of a triplet loss deep network setting for the forensic identification of speakers in Spanish. Within the framework, we train a convolutional network to produce vector representations of speech spectrogram slices. Then we test how similar these vectors are for a given speaker and how dissimilar are compared with other speakers. Based on these metrics we propose the calculation of the Likelihood Radio which is a cornerstone for forensic identification.
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Schulze, Lucas, Renan Sebem, and Douglas Wildgrube Bertol. "Performance of PSO and GWO Algorithms Applied in Text-Independent Speaker Identification." In Congresso Brasileiro de Inteligência Computacional. SBIC, 2021. http://dx.doi.org/10.21528/cbic2021-98.

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In this paper, we analyze the performance of two bio-inspired algorithms applied in text-independent speaker recognition through voice signal. The analyzed algorithms are particle swarm optimization and grey wolf optimization. The complete methodology described in this paper was specifically developed in the context of this work. First, a widely known model of the speaker is determined based on discrete transfer functions. Then a method of estimation of the input signal is determined. The bio-inspired algorithms are custom-developed and applied to parameterize the transfer functions based on t
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Jin, Qin, Szu-Chen Stan Jou, and Tanja Schultz. "Whispering Speaker Identification." In Multimedia and Expo, 2007 IEEE International Conference on. IEEE, 2007. http://dx.doi.org/10.1109/icme.2007.4284828.

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Jin, Qin, Tanja Schultz, and Alex Waibel. "Phonetic speaker identification." In 7th International Conference on Spoken Language Processing (ICSLP 2002). ISCA, 2002. http://dx.doi.org/10.21437/icslp.2002-409.

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Shahin, Ismail, and Mohammed Nasser Ba-Hutair. "Emarati speaker identification." In 2014 12th International Conference on Signal Processing (ICSP 2014). IEEE, 2014. http://dx.doi.org/10.1109/icosp.2014.7015053.

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Farrell, Kevin R., Khaled T. Assaleh, and Richard J. Mammone. "Speaker recognition using the neural tree network." In Substance Identification Technologies, edited by James L. Flanagan, Richard J. Mammone, Albert E. Brandenstein, et al. SPIE, 1994. http://dx.doi.org/10.1117/12.172517.

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Kuo, Jeff, Chin-Hui Lee, and Aaron E. Rosenberg. "Speaker set identification through speaker group modeling." In 2nd International Conference on Spoken Language Processing (ICSLP 1992). ISCA, 1992. http://dx.doi.org/10.21437/icslp.1992-372.

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Gish, H., M. H. Siu, and R. Rohlicek. "Segregation of speakers for speech recognition and speaker identification." In [Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing. IEEE, 1991. http://dx.doi.org/10.1109/icassp.1991.150477.

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Farjo, J., M. Hamouche J. Aoun, M. Hamad, and A. Kassem. "Speaker identification on compactRIO." In MELECON 2012 - 2012 16th IEEE Mediterranean Electrotechnical Conference. IEEE, 2012. http://dx.doi.org/10.1109/melcon.2012.6196458.

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Frati, Pasi, Evgeny Karpov, and Tomi Kinnunen. "Real-time speaker identification." In Interspeech 2004. ISCA, 2004. http://dx.doi.org/10.21437/interspeech.2004-520.

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Reports on the topic "Speaker identification"

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Yantorno, Robert E. Co-Channel Speech and Speaker Identification Study. Defense Technical Information Center, 1998. http://dx.doi.org/10.21236/ada399500.

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Jin, Qin, and Yun Wang. Integrated Robust Open-Set Speaker Identification System (IROSIS). Defense Technical Information Center, 2012. http://dx.doi.org/10.21236/ada562148.

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Cohen, Z. Noise Reduction with Microphone Arrays for Speaker Identification. Office of Scientific and Technical Information (OSTI), 2011. http://dx.doi.org/10.2172/1034487.

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Catellier, Andrew, and Stephen Voran. Relationships Between Intelligibility, Speaker Identification, and the Detection of Dramatized Urgency. Institute for Telecommunication Sciences, 2008. https://doi.org/10.70220/vplrkz37.

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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, 2005. http://dx.doi.org/10.17487/rfc4313.

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Mizrach, Amos, Michal Mazor, Amots Hetzroni, et al. Male Song as a Tool for Trapping Female Medflies. United States Department of Agriculture, 2002. http://dx.doi.org/10.32747/2002.7586535.bard.

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This interdisciplinaray work combines expertise in engineering and entomology in Israel and the US, to develop an acoustic trap for mate-seeking female medflies. Medflies are among the world's most economically harmful pests, and monitoring and control efforts cost about $800 million each year in Israel and the US. Efficient traps are vitally important tools for medfly quarantine and pest management activities; they are needed for early detection, for predicting dispersal patterns and for estimating medfly abundance within infested regions. Early detection facilitates rapid response to invasio
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Chong, Alberto E. Does It Matter How People Speak? Inter-American Development Bank, 2006. http://dx.doi.org/10.18235/0010970.

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Language serves two key functions. It enables communication between agents, which allows for the establishment and operation of formal and informal institutions. It also serves a less obvious function, a reassuring quality more closely related to issues linked with trust, social capital, and cultural identification. While research on the role of language as a learning process is widespread, there is no evidence on the role of language as a signal of cultural affinity. I pursue this latter avenue of research and show that subtle language affinity is positively linked with change in earnings whe
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Pinzón-Puerto, Freddy, and Mauricio Villamizar-Villegas. Do Actions Speak Louder than Words? A Foreign Exchange Intervention Analysis. Banco de la República Colombia, 2023. http://dx.doi.org/10.32468/be.1223.

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We revisit an old question but with a new identification strategy, namely the difference in exchange rate effects between announced (“vocal”) and secret (“dirty”) foreign exchange intervention. Using a Regression Discontinuity Design, we exploit a rule-based intervention mechanism enacted by the Central Bank of Colombia that, under observable and deterministic conditions, triggered either the issuance of FX options or the ability to exercise them. We take the former (issuance) as central bank announcements under a sharp setting, since the rule and information that triggered the issuance of opt
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