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Journal articles on the topic 'Keyword spotting'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Khan, Wasiq, and Rob Holton. "Decision Support System for Keyword Spotting Using Theory of Evidence." International Journal of Computer and Electrical Engineering 8, no. 1 (2016): 22–30. http://dx.doi.org/10.17706/ijcee.2016.8.1.22-30.

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12

Ben Ayed, Yassine. "A New SVM Kernel for Keyword Spotting Using Confidence Measures." International Journal on Artificial Intelligence Tools 24, no. 03 (2015): 1550010. http://dx.doi.org/10.1142/s0218213015500104.

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In this paper, we propose an alternative keyword spotting method relying on confidence measures and support vector machines. Confidence measures are computed from phone information provided by a Hidden Markov Model based speech recognizer. We use three kinds of techniques, i.e., arithmetic, geometric and harmonic means to compute a confidence measure for each word. The acceptance/rejection decision of a word is based on the confidence vector processed by the SVM classifier for which we propose a new Beta kernel. The performance of the proposed SVM classifier is compared with spotting methods b

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13

Wöllmer, Martin, Björn Schuller, and Gerhard Rigoll. "Keyword spotting exploiting Long Short-Term Memory." Speech Communication 55, no. 2 (2013): 252–65. http://dx.doi.org/10.1016/j.specom.2012.08.006.

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14

Benisty, Hadas, Itamar Katz, Koby Crammer, and David Malah. "Discriminative Keyword Spotting for limited-data applications." Speech Communication 99 (May 2018): 1–11. http://dx.doi.org/10.1016/j.specom.2018.02.003.

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15

Liu, Li, Mingxue Yang, Xinyi Gao, Qingsong Liu, Zhengxi Yuan, and Jun Zhou. "Keyword spotting techniques to improve the recognition accuracy of user-defined keywords." Neural Networks 139 (July 2021): 237–45. http://dx.doi.org/10.1016/j.neunet.2021.03.012.

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16

Laszko, Łukasz. "Experimental research on the impact of similarity function selection on the quality of keyword spotting in speech signal." Przegląd Teleinformatyczny 7(25), no. 3-4 (2019): 57–81. http://dx.doi.org/10.5604/01.3001.0013.6598.

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The paper describes an evaluation of the application of selected similarity functions in the task of keyword spotting. Experiments were carried out in the Polish language. The research results can be used to improve already existing keyword spotting methods, or to develop new ones.

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17

Wang, Yi, Junan Yang, Jingtao Liu, Qiang Chen, and Song Li. "Improved features using convolution-augmented transformers for keyword spotting." ITM Web of Conferences 47 (2022): 02039. http://dx.doi.org/10.1051/itmconf/20224702039.

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Transformer can effectively model long rang dependency, but suffer from uncapable to extract local feature patterns. While CNNs exploit local features effectively. In this paper, we seek to combine convolution and Transformers improves over using them individually, and propose improved features using convolution-augmented transformers for keyword spotting. The convolution-augmented transformers are constructed with a ResNet front-end and a convolution-augmented transformers back-end in series. Using this improved feature for keyword spotting task. The results show that the improved features us

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18

Bushur, Jacob, and Chao Chen. "Neural Network Exploration for Keyword Spotting on Edge Devices." Future Internet 15, no. 6 (2023): 219. http://dx.doi.org/10.3390/fi15060219.

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The introduction of artificial neural networks to speech recognition applications has sparked the rapid development and popularization of digital assistants. These digital assistants constantly monitor the audio captured by a microphone for a small set of keywords. Upon recognizing a keyword, a larger audio recording is saved and processed by a separate, more complex neural network. Deep neural networks have become an effective tool for keyword spotting. Their implementation in low-cost edge devices, however, is still challenging due to limited resources on board. This research demonstrates th

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19

Lei, Jie, Tousif Rahman, Rishad Shafik, et al. "Low-Power Audio Keyword Spotting Using Tsetlin Machines." Journal of Low Power Electronics and Applications 11, no. 2 (2021): 18. http://dx.doi.org/10.3390/jlpea11020018.

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The emergence of artificial intelligence (AI) driven keyword spotting (KWS) technologies has revolutionized human to machine interaction. Yet, the challenge of end-to-end energy efficiency, memory footprint and system complexity of current neural network (NN) powered AI-KWS pipelines has remained ever present. This paper evaluates KWS utilizing a learning automata powered machine learning algorithm called the Tsetlin Machine (TM). Through significant reduction in parameter requirements and choosing logic over arithmetic-based processing, the TM offers new opportunities for low-power KWS while

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20

Assadi, M., and M. M. Homayounpour. "Performance Improvement in Keyword Spotting for Telephony Services." International Journal of Computer Applications 77, no. 8 (2013): 18–22. http://dx.doi.org/10.5120/13414-1079.

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21

Sakurai, Mitsuyasu, and Yasuo Ariki. "Classification of news speech articles by keyword spotting." Journal of the Acoustical Society of America 100, no. 4 (1996): 2758. http://dx.doi.org/10.1121/1.416335.

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22

Li, Haiyang, Hao Yuan, Jiqing Han, and Tieran Zheng. "Beam Pruning Based on Quantile for Keyword Spotting." Procedia Engineering 29 (2012): 2985–89. http://dx.doi.org/10.1016/j.proeng.2012.01.426.

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23

Roy, Partha Pratim, Ayan Kumar Bhunia, Ayan Das, Prithviraj Dhar, and Umapada Pal. "Keyword spotting in doctor's handwriting on medical prescriptions." Expert Systems with Applications 76 (June 2017): 113–28. http://dx.doi.org/10.1016/j.eswa.2017.01.027.

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24

Hou, Jingyong, Yangyang Shi, Mari Ostendorf, Mei-Yuh Hwang, and Lei Xie. "Region Proposal Network Based Small-Footprint Keyword Spotting." IEEE Signal Processing Letters 26, no. 10 (2019): 1471–75. http://dx.doi.org/10.1109/lsp.2019.2936282.

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25

Tabibian, Shima. "A survey on structured discriminative spoken keyword spotting." Artificial Intelligence Review 53, no. 4 (2019): 2483–520. http://dx.doi.org/10.1007/s10462-019-09739-y.

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26

Knill, K. M., and S. J. Young. "Low-cost implementation of open set keyword spotting." Computer Speech & Language 13, no. 3 (1999): 243–66. http://dx.doi.org/10.1006/csla.1999.0122.

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27

Vreča, Jure, Ratko Pilipović, and Anton Biasizzo. "Hardware–Software Co-Design of an Audio Feature Extraction Pipeline for Machine Learning Applications." Electronics 13, no. 5 (2024): 875. http://dx.doi.org/10.3390/electronics13050875.

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Keyword spotting is an important part of modern speech recognition pipelines. Typical contemporary keyword-spotting systems are based on Mel-Frequency Cepstral Coefficient (MFCC) audio features, which are relatively complex to compute. Considering the always-on nature of many keyword-spotting systems, it is prudent to optimize this part of the detection pipeline. We explore the simplifications of the MFCC audio features and derive a simplified version that can be more easily used in embedded applications. Additionally, we implement a hardware generator that generates an appropriate hardware pi

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28

Seo, Deokjin, Heung-Seon Oh, and Yuchul Jung. "Wav2KWS: Transfer Learning From Speech Representations for Keyword Spotting." IEEE Access 9 (2021): 80682–91. http://dx.doi.org/10.1109/access.2021.3078715.

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29

ZHU, Guoteng, and Wei SUN. "Rapid speech keyword spotting method based on template matching." Journal of Computer Applications 33, no. 11 (2013): 3138–40. http://dx.doi.org/10.3724/sp.j.1087.2013.03138.

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30

Giraldo, J. S. P., and Marian Verhelst. "Hardware Acceleration for Embedded Keyword Spotting: Tutorial and Survey." ACM Transactions on Embedded Computing Systems 20, no. 6 (2021): 1–25. http://dx.doi.org/10.1145/3474365.

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In recent years, Keyword Spotting (KWS) has become a crucial human–machine interface for mobile devices, allowing users to interact more naturally with their gadgets by leveraging their own voice. Due to privacy, latency and energy requirements, the execution of KWS tasks on the embedded device itself instead of in the cloud, has attracted significant attention from the research community. However, the constraints associated with embedded systems, including limited energy, memory, and computational capacity, represent a real challenge for the embedded deployment of such interfaces. In this art

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31

Zeng, Mengjun, and Nanfeng Xiao. "Effective Combination of DenseNet and BiLSTM for Keyword Spotting." IEEE Access 7 (2019): 10767–75. http://dx.doi.org/10.1109/access.2019.2891838.

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32

Rusiñol, Marçal, David Aldavert, Ricardo Toledo, and Josep Lladós. "Efficient segmentation-free keyword spotting in historical document collections." Pattern Recognition 48, no. 2 (2015): 545–55. http://dx.doi.org/10.1016/j.patcog.2014.08.021.

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33

Kumar, Gaurav, and Venu Govindaraju. "Bayesian background models for keyword spotting in handwritten documents." Pattern Recognition 64 (April 2017): 84–91. http://dx.doi.org/10.1016/j.patcog.2016.06.030.

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34

Cho, Beom-Joon, and Jin H. Kim. "Print keyword spotting with dynamically synthesized pseudo 2D HMMs." Pattern Recognition Letters 25, no. 9 (2004): 999–1011. http://dx.doi.org/10.1016/j.patrec.2004.02.014.

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35

Tabibian, Shima, Ahmad Akbari, and Babak Nasersharif. "A fast hierarchical search algorithm for discriminative keyword spotting." Information Sciences 336 (April 2016): 45–59. http://dx.doi.org/10.1016/j.ins.2015.12.010.

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36

Bhunia, Ayan Kumar, Partha Pratim Roy, Aneeshan Sain, and Umapada Pal. "Zone-based keyword spotting in Bangla and Devanagari documents." Multimedia Tools and Applications 79, no. 37-38 (2020): 27365–89. http://dx.doi.org/10.1007/s11042-019-08442-y.

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37

Hao, Jie, and Xing Li. "Word spotting based ona posterior measure of keyword confidence." Journal of Computer Science and Technology 17, no. 4 (2002): 491–97. http://dx.doi.org/10.1007/bf02943289.

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38

Eng-Fong Huang, Hsiao-Chuan Wang, and F. K. Soong. "A fast algorithm for large vocabulary keyword spotting application." IEEE Transactions on Speech and Audio Processing 2, no. 3 (1994): 449–52. http://dx.doi.org/10.1109/89.294361.

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39

Brajen, Kumar Deka, and Das Pranab. "Isolated Keyword Spotting in Multilingual Environment using ANN and MFCC." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 4 (2020): 5–8. https://doi.org/10.35940/ijeat.C6135.049420.

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The performance and analysis of Keyword Spotting system (KWS) are applied when the training and testing in a multilingual environment. This paper exhibits an approach for building up a multilingual KWS framework for Assamese, English and Hindi language dependent on feed-forward neural system. Mel Frequency Cepstral Coefficient (MFCC) has been utilized for highlight extraction which gives a lot of highlight vectors from recorded sound examples. Neural Network backpropagation model is utilized to improve the acknowledgment execution on the recently made multilingual database utilizing the multi-

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40

Abbas, Nurul Atikah, and Mohd Ridzuan Ahmad. "KEYWORD SPOTTING SYSTEM WITH NANO 33 BLE SENSE USING EMBEDDED MACHINE LEARNING APPROACH." Jurnal Teknologi 85, no. 3 (2023): 175–82. http://dx.doi.org/10.11113/jurnalteknologi.v85.18744.

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Due to the obvious advancement of artificial intelligence, keyword spotting has become a fast-growing technology that was first launched a few years ago by hidden Markov models. Keyword spotting is the technique of finding terms that have been pre-programmed into a machine learning model. However, because the keyword spotting system model will be installed on a small and resource-constrained device, it must be minimal in size. It is difficult to maintain accuracy and performance when minimizing the model size. We suggested in this paper to develop a TinyML model that responds to voice commands

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41

Nabaz, Danyar, Noraimi Shafie, and Azizul Azizan. "Design Of Emergency Keyword Recognition Using Arduino Nano BLE Sense 33 And Edge Impulse." Open International Journal of Informatics 11, no. 2 (2023): 46–57. http://dx.doi.org/10.11113/oiji2023.11n2.271.

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This project focuses on Custom Keyword Voice Recognition (CKVR) for emergency response scenarios. A multilingual keyword spotting system is developed using the Arduino Nano 33 BLE Sense board and Edge Impulse. The system accurately recognizes the keyword "help" in English, Arabic, Kurdish, and Malay languages. The project utilizes Mel Frequency Cepstral Coefficients (MFCC) for feature extraction and employs deep learning techniques for model training. By optimizing the model through quantization and achieving 100% accuracy in training and testing phases, the system provides a reliable solution

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42

Xuan. "AN EVALUATION OF SOME FACTORS AFFECTING ACCURACY OF THE VIETNAMESE KEYWORD SPOTTING SYSTEM." Journal of Military Science and Technology, no. 67 (June 12, 2020): 33–43. http://dx.doi.org/10.54939/1859-1043.j.mst.67.2020.33-43.

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Keyword spotting (KWS) is one of the important systems on speech applications, such as data mining, call routing, call center, customer-controlled smartphone, smart home systems with voice control, etc. With the goals of researching some factors affecting the Vietnamese Keyword spotting system, we study the combination architecture of CNN (Convolutional Neural Networks)-RNN (Recurrent Neural Networks) on both clean and noise environments with 2 distance speaker cases: 1m and 2m. The obtained results show that the noise trained models are better performance than clean trained models in any (cle

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43

Daraee, Fatemeh, Saeed Mozaffari, and Seyyed Mohammad Razavi. "Handwritten keyword spotting using deep neural networks and certainty prediction." Computers & Electrical Engineering 92 (June 2021): 107111. http://dx.doi.org/10.1016/j.compeleceng.2021.107111.

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44

S., Sushma, and Sharada B. "Keyword Spotting in Scanned Images of Historical Handwritten Devanagri Documents." International Journal of Computer Applications 181, no. 36 (2019): 5–9. http://dx.doi.org/10.5120/ijca2019918322.

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45

Deka, B. K., and P. Das. "A Review of Keyword Spotting as an Audio Mining Technique." International Journal of Computer Sciences and Engineering 7, no. 1 (2019): 757–69. http://dx.doi.org/10.26438/ijcse/v7i1.757769.

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46

Park, Sang-Cheol, Soo-Hyung Kim, and Deok-Jai Choi. "Keyword Spotting on Hangul Document Images Using Character Feature Models." KIPS Transactions:PartB 12B, no. 5 (2005): 521–26. http://dx.doi.org/10.3745/kipstb.2005.12b.5.521.

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47

Giraldo, J. S. P., Vikram Jain, and Marian Verhelst. "Efficient Execution of Temporal Convolutional Networks for Embedded Keyword Spotting." IEEE Transactions on Very Large Scale Integration (VLSI) Systems 29, no. 12 (2021): 2220–28. http://dx.doi.org/10.1109/tvlsi.2021.3120189.

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48

Pattanayak, Biswaranjan, Jayant Kumar Rout, and Gayadhar Pradhan. "Adaptive spectral smoothening for development of robust keyword spotting system." IET Signal Processing 13, no. 5 (2019): 544–50. http://dx.doi.org/10.1049/iet-spr.2019.0027.

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49

Lopez-Espejo, Ivan, Zheng-Hua Tan, and Jesper Jensen. "Improved External Speaker-Robust Keyword Spotting for Hearing Assistive Devices." IEEE/ACM Transactions on Audio, Speech, and Language Processing 28 (2020): 1233–47. http://dx.doi.org/10.1109/taslp.2020.2984089.

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50

Stauffer, Michael, Andreas Fischer, and Kaspar Riesen. "Keyword spotting in historical handwritten documents based on graph matching." Pattern Recognition 81 (September 2018): 240–53. http://dx.doi.org/10.1016/j.patcog.2018.04.001.

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