Relevant bibliographies by topics / Keyword spotting

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Academic literature on the topic 'Keyword spotting'

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

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Journal articles on the topic "Keyword spotting"

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Keshet, Joseph, David Grangier, and Samy Bengio. "Discriminative keyword spotting." Speech Communication 51, no. 4 (2009): 317–29. http://dx.doi.org/10.1016/j.specom.2008.10.002.

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Retsinas, George, Georgios Louloudis, Nikolaos Stamatopoulos, and Basilis Gatos. "Efficient Learning-Free Keyword Spotting." IEEE Transactions on Pattern Analysis and Machine Intelligence 41, no. 7 (2019): 1587–600. http://dx.doi.org/10.1109/tpami.2018.2845880.

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Ke, Dengfeng, Baole Du, Yunjia Tong, and Yanyan Xu. "Broadcast Attention Learning for Real Telephone Speech Keyword Spotting." Journal of Physics: Conference Series 2506, no. 1 (2023): 012003. http://dx.doi.org/10.1088/1742-6596/2506/1/012003.

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Abstract With the development of mobile smart devices, keyword spotting plays an important role in the interaction between machines and users. However, low storage and low energy consumption of mobile devices limit the accuracies of keyword spotting tasks. Therefore, how to achieve a balance between the high accuracy and low consumption is a research hotspot for a keyword spotting system. Convolutional neural networks have been widely adopted in recent keyword spotting systems due to their superior accuracies, and the success of the transformer architecture in many areas demonstrates the effec
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Liu, Zuozhen, Ta Li, and Pengyuan Zhang. "Neural keyword confidence estimation for open‐vocabulary keyword spotting." Electronics Letters 58, no. 3 (2021): 133–35. http://dx.doi.org/10.1049/ell2.12368.

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Lopez-Espejo, Ivan, Zheng-Hua Tan, John H. L. Hansen, and Jesper Jensen. "Deep Spoken Keyword Spotting: An Overview." IEEE Access 10 (2022): 4169–99. http://dx.doi.org/10.1109/access.2021.3139508.

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Retsinas, George, Giorgos Sfikas, and Basilis Gatos. "Transferable Deep Features for Keyword Spotting." Proceedings 2, no. 2 (2018): 89. http://dx.doi.org/10.3390/proceedings2020089.

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Brik, Youcef. "Mental model for handwritten keyword spotting." Journal of Electronic Imaging 27, no. 05 (2018): 1. http://dx.doi.org/10.1117/1.jei.27.5.053027.

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Yamashita, Yoichi, Daisuke Iwahashi, and Riichiro Mizoguchi. "Keyword spotting using F0 contour information." Systems and Computers in Japan 32, no. 7 (2001): 52–61. http://dx.doi.org/10.1002/scj.1041.

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Rebai, Ilyes, Yassine BenAyed, and Walid Mahdi. "A novel keyword rescoring method for improved spoken keyword spotting." Procedia Computer Science 126 (2018): 312–20. http://dx.doi.org/10.1016/j.procs.2018.07.265.

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Kanadje, Manish, Zachary Miller, Anurag Agarwal, Roger Gaborski, Richard Zanibbi, and Stephanie Ludi. "Assisted keyword indexing for lecture videos using unsupervised keyword spotting." Pattern Recognition Letters 71 (February 2016): 8–15. http://dx.doi.org/10.1016/j.patrec.2015.11.012.

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Dissertations / Theses on the topic "Keyword spotting"

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Skácel, Miroslav. "Query-by-Example Keyword Spotting." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2015. http://www.nusl.cz/ntk/nusl-234939.

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Tato diplomová práce se zabývá moderními přístupy detekce klíčových slov a detekce frází v řečových datech. V úvodní části je seznámení s problematikou a teoretický popis metod pro detekci. Následuje popis reprezentace vstupních datových sad použitých při experimentech a evaluaci. Dále jsou uvedeny metody pro detekci klíčových slov definovaných vzorem. Následně jsou popsány evaluační metody a techniky použité pro skórování. Po provedení experimentů na datových sadách a po evaluaci jsou diskutovány výsledky. V dalším kroku jsou navrženy a poté implementovány moderní postupy vedoucí k vylepšení
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Sunde, Valfridsson Jonas. "Query By Example Keyword Spotting." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-299743.

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Voice user interfaces have been growing in popularity and with them an interest for open vocabulary keyword spotting. In this thesis we focus on one particular approach to open vocabulary keyword spotting, query by example keyword spotting. Three types of query by example keyword spotting approaches are described and evaluated: sequence distances, speech to phonemes and deep distance learning. Evaluation is done on a series of custom tasks designed to measure a variety of aspects. The Google Speech Commands benchmark is used for evaluation as well, this to make it more comparable to existing w
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Ling, Yong. "Keyword spotting in continuous speech utterances." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape7/PQDD_0024/MQ50822.pdf.

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Ling, Yong 1973. "Keyword spotting in continuous speech utterances." Thesis, McGill University, 1999. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=21595.

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The work in this thesis constructed a word spotting system, which managed to spot an amount of pre-defined keywords out of unconstrained running conversational speech utterances. The development and experiments are based on the Credit Card subset of SWITCHBOARD speech corpus. The techniques are applied in the context of a Hidden Markov Model (HMM) based Continuous Speech Recognition (CSR) approach to keyword spotting. The word spotting system uses context-dependent acoustic triphone to model both keyword and non-keyword speech utterances. To enhance the true keyword spotting rate, sophisticate
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Puigcerver, I. Pérez Joan. "A Probabilistic Formulation of Keyword Spotting." Doctoral thesis, Universitat Politècnica de València, 2019. http://hdl.handle.net/10251/116834.

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[ES] La detección de palabras clave (Keyword Spotting, en inglés), aplicada a documentos de texto manuscrito, tiene como objetivo recuperar los documentos, o partes de ellos, que sean relevantes para una cierta consulta (query, en inglés), indicada por el usuario, entre una gran colección de documentos. La temática ha recogido un gran interés en los últimos 20 años entre investigadores en Reconocimiento de Formas (Pattern Recognition), así como bibliotecas y archivos digitales. Esta tesis, en primer lugar, define el objetivo de la detección de palabras clave a partir de una perspectiva basada
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Wang, Miaorong. "Algorithms and low power hardware for keyword spotting." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/118035.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 73-76).<br>Keyword spotting (KWS) is widely used in mobile devices to provide hands-free interface. It continuously listens to all sound signals, detects specific keywords and triggers the downstream system. The key design target of a KWS system is to achieve high classification accuracy of specified keywords and have low power consumption while doing real-time processing of speech data. The
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Friesch, Pius. "Generating Training Data for Keyword Spotting given Few Samples." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254960.

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Speech recognition systems generally need a large quantity of highly variable voice and recording conditions in order to produce robust results. In the specific case of keyword spotting, where only short commands are recognized instead of large vocabularies, the resource-intensive task of data acquisition has to be repeated for each keyword individually. Over the past few years, neural methods in speech synthesis and voice conversion made tremendous progress and generate samples that are realistic to the human ear. In this work, we explore the feasibility of using such methods to generate trai
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Zhang, Yaodong Ph D. Massachusetts Institute of Technology. "Unsupervised spoken keyword spotting and learning of acoustically meaningful units." Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/54655.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (p. 103-106).<br>The problem of keyword spotting in audio data has been explored for many years. Typically researchers use supervised methods to train statistical models to detect keyword instances. However, such supervised methods require large quantities of annotated data that is unlikely to be available for the majority of languages in the world. This thesis addresses this lack-of-annotation problem
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Narasimhan, Karthik Rajagopal. "Morphological segmentation : an unsupervised method and application to Keyword Spotting." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/90139.

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Thesis: S.M. in Computer Science and Engineering, Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2014.<br>26<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 41-44).<br>The contributions of this thesis are twofold. First, we present a new unsupervised algorithm for morphological segmentation that utilizes pseudo-semantic information, in addition to orthographic cues. We make use of the semantic signals from continuous word vectors, trained on huge corpora of raw text data. We formulate a log-linear model t
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Thambiratnam, Albert J. K. "Acoustic keyword spotting in speech with applications to data mining." Thesis, Queensland University of Technology, 2005. https://eprints.qut.edu.au/37254/1/Albert_Thambiratnam_Thesis.pdf.

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Keyword Spotting is the task of detecting keywords of interest within continu- ous speech. The applications of this technology range from call centre dialogue systems to covert speech surveillance devices. Keyword spotting is particularly well suited to data mining tasks such as real-time keyword monitoring and unre- stricted vocabulary audio document indexing. However, to date, many keyword spotting approaches have su®ered from poor detection rates, high false alarm rates, or slow execution times, thus reducing their commercial viability. This work investigates the application of keyword spot
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Books on the topic "Keyword spotting"

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Stauffer, Michael, Andreas Fischer, and Kaspar Riesen. Graph-Based Keyword Spotting. WORLD SCIENTIFIC, 2019. http://dx.doi.org/10.1142/11452.

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Graph-Based Keyword Spotting. World Scientific Publishing Co Pte Ltd, 2019.

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Book chapters on the topic "Keyword spotting"

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Moyal, Ami, Vered Aharonson, Ella Tetariy, and Michal Gishri. "Keyword Spotting Methods." In SpringerBriefs in Electrical and Computer Engineering. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-6489-1_2.

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Mary, Leena, and Deekshitha G. "Keyword Spotting Techniques." In SpringerBriefs in Speech Technology. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-97761-4_4.

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Veiga, Arlindo, Carla Lopes, Luís Sá, and Fernando Perdigão. "Acoustic Similarity Scores for Keyword Spotting." In Lecture Notes in Computer Science. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09761-9_5.

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Bhardwaj, Anurag, Srirangaraj Setlur, and Venu Govindaraju. "Keyword Spotting Techniques for Sanskrit Documents." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-00155-0_22.

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Moyal, Ami, Vered Aharonson, Ella Tetariy, and Michal Gishri. "Keyword Spotting Out of Continuous Speech." In SpringerBriefs in Electrical and Computer Engineering. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-6489-1_1.

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Ayed, Yassine Ben, Dominique Fohr, Jean Paul Haton, and Gérard Chollet. "Keyword Spotting Using Support Vector Machines." In Text, Speech and Dialogue. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-46154-x_39.

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Sfikas, Giorgos, George Retsinas, Angelos P. Giotis, Basilis Gatos, and Christophoros Nikou. "Keyword Spotting with Quaternionic ResNet: Application to Spotting in Greek Manuscripts." In Document Analysis Systems. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-06555-2_26.

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Lleida, E., J. B. Mariño, J. Salavedra, and A. Moreno. "Keyword Spotting, an Application for Voice Dialing." In Speech Recognition and Coding. Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/978-3-642-57745-1_40.

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Greibus, Mindaugas, and Laimutis Telksnys. "Speech Keyword Spotting with Rule Based Segmentation." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41947-8_17.

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Gales, Mark J. F., Kate M. Knill, and Anton Ragni. "Low-Resource Speech Recognition and Keyword-Spotting." In Speech and Computer. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-66429-3_1.

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Conference papers on the topic "Keyword spotting"

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Labrador, Beltrán, Pai Zhu, Guanlong Zhao, et al. "Personalizing Keyword Spotting with Speaker Information." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10890730.

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Peng, Tianyi, and Yang Xiao. "Dark Experience for Incremental Keyword Spotting." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10890228.

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Qiutian, Li. "Lightweight Computational Model for Keyword Spotting." In 2024 21st International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP). IEEE, 2024. https://doi.org/10.1109/iccwamtip64812.2024.10873662.

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Xu, Anfeng, Biqiao Zhang, Shuyu Kong, et al. "Effective Integration of KAN for Keyword Spotting." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10890453.

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Chen, Yongqiang, Qianhua He, Yanxiong Li, Zunxian Liu, Mingru Yang, and Jinxin Huang. "Deep Enhancement Spotting Network for Low-complexity Keyword Spotting in Noisy Environments." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10890683.

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Haznedaroglu, Ali, Osman Buyuk, and Levent M. Arslan. "Keyword Spotting using Keyword Adapted Language Model." In 2007 15th IEEE Signal Processing and Communications Applications. IEEE, 2007. http://dx.doi.org/10.1109/siu.2007.4298570.

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Chen, Qiyu, Weibin Zhang, Xiangmin Xu, and Xiaofen Xing. "Improved keyword spotting based on keyword/garbage models." In 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA). IEEE, 2016. http://dx.doi.org/10.1109/apsipa.2016.7820743.

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Li, L., S. J. Lu, and C. L. Tan. "A Fast Keyword-Spotting Technique." In Ninth International Conference on Document Analysis and Recognition (ICDAR 2007). IEEE, 2007. http://dx.doi.org/10.1109/icdar.2007.4378677.

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Bahi, Halima, and Nadia Benati. "A new keyword spotting approach." In 2009 International Conference on Multimedia Computing and Systems (ICMCS). IEEE, 2009. http://dx.doi.org/10.1109/mmcs.2009.5256728.

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Huh, Jaesung, Minjae Lee, Heesoo Heo, Seongkyu Mun, and Joon Son Chung. "Metric Learning for Keyword Spotting." In 2021 IEEE Spoken Language Technology Workshop (SLT). IEEE, 2021. http://dx.doi.org/10.1109/slt48900.2021.9383571.

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Reports on the topic "Keyword spotting"

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Mitra, Vikramjit, Julien van Hout, Horacio Franco, et al. Feature Fusion for High-Accuracy Keyword Spotting. Defense Technical Information Center, 2014. http://dx.doi.org/10.21236/ada613972.

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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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