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Journal articles on the topic 'Phone recognition'

Author: Grafiati

Published: 4 June 2021

Last updated: 31 January 2023

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1

Lin, Jhe-Syuan, and Wen-Shing Sun. "A Hidden Fingerprint Device on an Opaque Display Panel." Applied Sciences 10, no. 6 (March 23, 2020): 2188. http://dx.doi.org/10.3390/app10062188.

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In recent years, fingerprint recognition has become more and more widely used in mobile phones. A fingerprint recognition device hidden under an opaque display panel designed based on a waveguide and frustrated total internal reflection (FTIR) is proposed and demonstrated herein. In order to meet the demand for a high screen ratio for mobile phone displays, we use a symmetrical zoom-in and zoom-out coupler design. With this comprehensive coupler and waveguide design, not only can fingerprint recognition be achieved using an opaque display panel, but it also meets the appearance requirements for a mobile phone with a high screen ratio.

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2

Zeng, Hong, Yidan Hu, Jin Fan, Haiyang Hu, Zhigang Gao, and Qiming Fang. "Arm Motion Recognition and Exercise Coaching System for Remote Interaction." Mobile Information Systems 2016 (2016): 1–14. http://dx.doi.org/10.1155/2016/9849720.

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Arm motion recognition and its related applications have become a promising human computer interaction modal due to the rapid integration of numerical sensors in modern mobile-phones. We implement a mobile-phone-based arm motion recognition and exercise coaching system that can help people carrying mobile-phones to do body exercising anywhere at any time, especially for the persons that have very limited spare time and are constantly traveling across cities. We first designimproved k-meansalgorithm to cluster the collecting 3-axis acceleration and gyroscope data of person actions into basic motions. A learning method based on Hidden Markov Model is then designed to classify and recognize continuous arm motions of both learners and coaches, which also measures the action similarities between the persons. We implement the system on MIUI 2S mobile-phone and evaluate the system performance and its accuracy of recognition.

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3

Yang, Gang, and Jia Ni Luo. "A Real-Time Face Recognition System for Android Smart Phone." Advanced Materials Research 756-759 (September 2013): 4006–10. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.4006.

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With the widely application of face recognition and the rapid development of Android OS, technique of face detection and recognition based on Android platform becomes increasingly attractive. This paper presents a real-time face recognition system on Android platform. The system realizes face detection by applying AdaBoost algorithm and face recognition by utilizing Eigenfaces. This paper also came up with some methods to speed up the face detection and recognition process and improve the correct rate of face recognition. Experimental results show that this system is able to realize real-time face detection and recognition on Android smart phones. In addition, all the work is completed on the smart phone without using any other terminals or tools.

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4

Yousef, Rana Mohammad, Omar Adwan, and Murad Abu-Leil. "An Enhanced Mobile Phone Dialler Application for Blind and Visually Impaired People." International Journal of Engineering & Technology 2, no. 4 (November 14, 2013): 270. http://dx.doi.org/10.14419/ijet.v2i4.1101.

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This paper presents the development of a new mobile phone dialler application which is designed to help blind and visually impaired people make phone calls. The new mobile phone dialler application is developed as a windows phone application to facilitate entering information to touch screen mobile phones by blind people. This application is advantageous through its innovative concept, its simplicity and its availability at an affordable cost. Feedback from users showed that this new application is easy to use and solves many problems of voice recognition applications such as inaccuracy, slowness and interpretation of unusual voices. In addition, this application has increased the users ability to dial phone numbers more independently and less stressfully.

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5

WANG, KONGQIAO, YANMING ZOU, and HAO WANG. "1D BAR CODE READING ON CAMERA PHONES." International Journal of Image and Graphics 07, no. 03 (July 2007): 529–50. http://dx.doi.org/10.1142/s0219467807002805.

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The availability of camera phones provides people with a mobile platform for decoding bar codes, whereas conventional scanners lack mobility. However, using a normal camera phone in such applications is challenging due to the out-of-focus problem. In this paper, we present the research effort on the bar code reading algorithms using a VGA camera phone, NOKIA 7650. EAN-13, a widely used 1D bar code standard, is taken as an example to show the efficiency of the method. A wavelet-based bar code region location and knowledge-based bar code segmentation scheme is applied to extract bar code characters from poor-quality images. All the segmented bar code characters are input to the recognition engine, and based on the recognition distance, the bar code character string with the smallest total distance is output as the final recognition result of the bar code. In order to train an efficient recognition engine, the modified Generalized Learning Vector Quantization (GLVQ) method is designed for optimizing a feature extraction matrix and the class reference vectors. 19 584 samples segmented from more than 1000 bar code images captured by NOKIA 7650 are involved in the training process. Testing on 292 bar code images taken by the same phone, the correct recognition rate of the entire bar code set reaches 85.62%. We are confident that auto focus or macro modes on camera phones will bring the presented method into real world mobile use.

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6

Hải Dương, Nguyễn, and Nguyễn Hồng Quang. "Vietnamese speech recognition on mobile phone." Journal of Science, Educational Science 60, no. 7A (2015): 180–88. http://dx.doi.org/10.18173/2354-1075.2015-0065.

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7

Balaraman, Mridul, Sorin Dusan, and James L. Flanagan. "Supplementary features for improving phone recognition." Journal of the Acoustical Society of America 116, no. 4 (October 2004): 2479. http://dx.doi.org/10.1121/1.4784901.

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8

Kwapisz, Jennifer R., Gary M. Weiss, and Samuel A. Moore. "Activity recognition using cell phone accelerometers." ACM SIGKDD Explorations Newsletter 12, no. 2 (March 31, 2011): 74–82. http://dx.doi.org/10.1145/1964897.1964918.

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9

van Alphen, Paul. "Phone recognition in continuous speech (Dutch)." Journal of the Acoustical Society of America 87, S1 (May 1990): S107. http://dx.doi.org/10.1121/1.2027812.

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10

Xing, Jian, Miao Yu, Shupeng Wang, Yaru Zhang, and Yu Ding. "Automated Fraudulent Phone Call Recognition through Deep Learning." Wireless Communications and Mobile Computing 2020 (August 28, 2020): 1–9. http://dx.doi.org/10.1155/2020/8853468.

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Several studies have shown that the phone number and call behavior generated by a phone call reveal the type of phone call. By analyzing the phone number rules and call behavior patterns, we can recognize the fraudulent phone call. The success of this recognition heavily depends on the particular set of features that are used to construct the classifier. Since these features are human-labor engineered, any change introduced to the telephone fraud can render these carefully constructed features ineffective. In this paper, we show that we can automate the feature engineering process and, thus, automatically recognize the fraudulent phone call by applying our proposed novel approach based on deep learning. We design and construct a new classifier based on Call Detail Records (CDR) for fraudulent phone call recognition and find that the performance achieved by our deep learning-based approach outperforms competing methods. Experimental results demonstrate the effectiveness of the proposed approach. Specifically, in our accuracy evaluation, the obtained accuracy exceeds 99%, and the most performant deep learning model is 4.7% more accurate than the state-of-the-art recognition model on average. Furthermore, we show that our deep learning approach is very stable in real-world environments, and the implicit features automatically learned by our approach are far more resilient to dynamic changes of a fraudulent phone number and its call behavior over time. We conclude that the ability to automatically construct the most relevant phone number features and call behavior features and perform accurate fraudulent phone call recognition makes our deep learning-based approach a precise, efficient, and robust technique for fraudulent phone call recognition.

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11

Zhu, Xiaolin, and Wei Lv. "Intelligent Analysis of Core Identification Based on Intelligent Algorithm of Core Identification." Discrete Dynamics in Nature and Society 2021 (November 28, 2021): 1–10. http://dx.doi.org/10.1155/2021/5242930.

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The communication recognition of mobile phone core is a test of the development of machine vision. The size of mobile phone core is very small, so it is difficult to identify small defects. Based on the in-depth study of the algorithm, combined with the actual needs of core identification, this paper improves the algorithm and proposes an intelligent algorithm suitable for core identification. In addition, according to the actual needs of core wire recognition, this paper makes an intelligent analysis of the core wire recognition process. In addition, this paper improves the traditional communication image recognition algorithm and analyzes the data of the recognition algorithm according to the shape and image characteristics of the mobile phone core. Finally, after constructing the functional structure of the system model constructed in this paper, the system model is verified and analyzed, and on this basis, the performance of the improved core recognition algorithm proposed in this paper is verified and analyzed. From the results of online monitoring and recognition, the statistical accuracy of mobile phone core video recognition is about 90%, which has higher accuracy in mobile phone core image recognition than traditional recognition algorithms. The core line recognition algorithm based on deep learning and machine vision is effective and has a good practical effect.

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12

Ahn, Junho, and Richard Han. "Personalized Behavior Pattern Recognition and Unusual Event Detection for Mobile Users." Mobile Information Systems 9, no. 2 (2013): 99–122. http://dx.doi.org/10.1155/2013/360243.

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Mobile phones have become widely used for obtaining help in emergencies, such as accidents, crimes, or health emergencies. The smartphone is an essential device that can record emergency situations, which can be used for clues or evidence, or as an alert system in such situations. In this paper, we focus on mobile-based identification of potentially unusual, or abnormal events, occurring in a mobile user's daily behavior patterns. For purposes of this research, we have classified events as “unusual” for a mobile user when an event is an infrequently occurring one from the user's normal behavior patterns–all of which are collected and recorded on a user's mobile phone. We build a general unusual event classification model to be automated on the smartphone for use by any mobile phone users. To classify both normal and unusual events, we analyzed the activity, location, and audio sensor data collected from 20 mobile phone users to identify these users' personalized normal daily behavior patterns and any unusual events occurring in their daily activity. We used binary fusion classification algorithms on the subjects' recorded experimental data and ultimately identified the most accurately performing fusion algorithm for unusual event detection.

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13

KHALIL, MOHAMMED S., FAJRI KURNIAWAN, and KASHIF SALEEM. "AUTHENTICATION OF FINGERPRINT BIOMETRICS ACQUIRED USING A CELLPHONE CAMERA: A REVIEW." International Journal of Wavelets, Multiresolution and Information Processing 11, no. 05 (September 2013): 1350033. http://dx.doi.org/10.1142/s0219691313500331.

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Over the past decade, there have been dramatic increases in the usage of mobile phones in the world. Currently available smart mobile phones are capable of storing enormous amounts of personal information/data. The smart mobile phone is also capable of connecting to other devices, with the help of different applications. Consequently, with these connections comes the requirement of security to protect personal information. Nowadays, in many applications, a biometric fingerprint recognition system has been embedded as a primary security measure. To enable a biometric fingerprint recognition system in smart mobile phones, without any additional costs, a built-in high performance camera can be utilized. The camera can capture the fingerprint image and generate biometric traits that qualify the biometric fingerprint authentication approach. However, the images acquired by a mobile phone are entirely different from the images obtained by dedicated fingerprint sensors. In this paper, we present the current trend in biometric fingerprint authentication techniques using mobile phones and explore some of the future possibilities in this field.

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14

Franco, Horacio, Harry Bratt, Romain Rossier, Venkata Rao Gadde, Elizabeth Shriberg, Victor Abrash, and Kristin Precoda. "EduSpeak®: A speech recognition and pronunciation scoring toolkit for computer-aided language learning applications." Language Testing 27, no. 3 (July 2010): 401–18. http://dx.doi.org/10.1177/0265532210364408.

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SRI International’s EduSpeak® system is a software development toolkit that enables developers of interactive language education software to use state-of-the-art speech recognition and pronunciation scoring technology. Automatic pronunciation scoring allows the computer to provide feedback on the overall quality of pronunciation and to point to specific production problems. We review our approach to pronunciation scoring, where our aim is to estimate the grade that a human expert would assign to the pronunciation quality of a paragraph or a phrase. Using databases of nonnative speech and corresponding human ratings at the sentence level, we evaluate different machine scores that can be used as predictor variables to estimate pronunciation quality. For more specific feedback on pronunciation, the EduSpeak toolkit supports a phone-level mispronunciation detection functionality that automatically flags specific phone segments that have been mispronounced. Phone-level information makes it possible to provide the student with feedback about specific pronunciation mistakes.Two approaches to mispronunciation detection were evaluated in a phonetically transcribed database of 130,000 phones uttered in continuous speech sentences by 206 nonnative speakers. Results show that classification error of the best system, for the phones that can be reliably transcribed, is only slightly higher than the average pairwise disagreement between the human transcribers.

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15

Zhang, Lian Hai, Qi Chen, and Dan Qu. "Adjustment Method between Phonological Attributes and Phone Boundaries." Applied Mechanics and Materials 433-435 (October 2013): 316–21. http://dx.doi.org/10.4028/www.scientific.net/amm.433-435.316.

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Two kinds of imperfections, namely the detection errors and the asynchrony between phonological attributes and phone boundaries, can cause a substantial decline in recognition accuracy of a detection-based automatic speech recognition system. To solve these problems, an adjustment method between phonological attributes and phone boundaries is proposed in this paper. At first the prior knowledge of corpus and the detection results are combined, then the asynchronies in the phone boundary area are compensated and the detection errors are corrected; additionally, by selectively deleting some frames with errors, the precision of the phone models are improved. After adoption of this adjustment method, 1.4% of phoneme recognition rate can be improved in the TIMIT phone classification experiments based on Conditional Random Fields.

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16

Guo, De Feng, Bin Liu, Xiao Tian Jin, and Hong Jian Liu. "Human Activity Recognition Using Smart-Phone Sensors." Applied Mechanics and Materials 571-572 (June 2014): 1019–29. http://dx.doi.org/10.4028/www.scientific.net/amm.571-572.1019.

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Activity recognition is a challenging problem for context-aware systems and applications. Many studies in this field has mainly adopted techniques based on supervised or semi-supervised learning algorithms to recognize activities by movement patterns gathered through sensors, but these existing systems suffer from complex issues for feature representations of sensor data and multi-sensors integration. In this paper, we propose a novel feature learning method for activity recognition based on entropy and construct an activity recognition model with multi-class AdaBoost algorithm. Experiments on sensor data from a real dataset demonstrate the significant potential of our method to extract features for activity recognition. The experimental results also show recognition model based on multi-class AdaBoost is effective. The average precision and recall for six activities are 95.9% and 95.9%, respectively, which are higher than results obtained by using other methods such as Support Vector Machine (SVM) or K-Nearest Neighbor (KNN).

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17

Chen, Zhehuai, Yimeng Zhuang, Yanmin Qian, and Kai Yu. "Phone Synchronous Speech Recognition With CTC Lattices." IEEE/ACM Transactions on Audio, Speech, and Language Processing 25, no. 1 (January 2017): 90–101. http://dx.doi.org/10.1109/taslp.2016.2625459.

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18

Rufiner, Hugo L., and John C. Goddard. "Method of wavelet selection in phone recognition." Computer Standards & Interfaces 20, no. 6-7 (March 1999): 456. http://dx.doi.org/10.1016/s0920-5489(99)90973-x.

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19

Manjunath, K. E., and K. Sreenivasa Rao. "Source and system features for phone recognition." International Journal of Speech Technology 18, no. 2 (December 9, 2014): 257–70. http://dx.doi.org/10.1007/s10772-014-9266-0.

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20

Lin, Xiaodan, Jianqing Zhu, and Donghua Chen. "Subband Aware CNN for Cell-Phone Recognition." IEEE Signal Processing Letters 27 (2020): 605–9. http://dx.doi.org/10.1109/lsp.2020.2985594.

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21

Cao, Junkuo, Mingcai Lin, Han Wang, Jiacheng Fang, and Yueshen Xu. "Towards Activity Recognition through Multidimensional Mobile Data Fusion with a Smartphone and Deep Learning." Mobile Information Systems 2021 (April 20, 2021): 1–11. http://dx.doi.org/10.1155/2021/6615695.

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The field of activity recognition has evolved relatively early and has attracted countless researchers. With the continuous development of science and technology, people’s research on human activity recognition is also deepening and becoming richer. Nowadays, whether it is medicine, education, sports, or smart home, various fields have developed a strong interest in activity recognition, and a series of research results have also been put into people’s real production and life. Nowadays, smart phones have become quite popular, and the technology is becoming more and more mature, and various sensors have emerged at the historic moment, so the related research on activity recognition based on mobile phone sensors has its necessity and possibility. This article will use an Android smartphone to collect the data of six basic behaviors of human, which are walking, running, standing, sitting, going upstairs, and going downstairs, through its acceleration sensor, and use the classic model of deep learning CNN (convolutional neural network) to fuse those multidimensional mobile data, using TensorFlow for model training and test evaluation. The generated model is finally transplanted to an Android phone to complete the mobile-end activity recognition system.

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22

Everhart, Charles A. "Advanced voice recognition phone interface for in-vehicle speech recognition applications." Journal of the Acoustical Society of America 118, no. 1 (2005): 29. http://dx.doi.org/10.1121/1.1999433.

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23

Nahamoo, David. "Reduction of search space in speech recognition using phone boundaries and phone ranking." Journal of the Acoustical Society of America 104, no. 5 (November 1998): 2558. http://dx.doi.org/10.1121/1.423802.

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24

GAUVAIN, J. L., L. F. LAMEL, G. ADDA, and J. MARIANI. "SPEECH-TO-TEXT CONVERSION IN FRENCH." International Journal of Pattern Recognition and Artificial Intelligence 08, no. 01 (February 1994): 99–131. http://dx.doi.org/10.1142/s021800149400005x.

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Speech-to-text conversion of French necessitates that both the acoustic level recognition and language modeling be tailored to the French language. Work in this area was initiated at LIMSI over 10 years ago. In this paper a summary of the ongoing research in this direction is presented. Included are studies on distributional properties of French text materials; problems specific to speech-to-text conversion particular of French; studies in phoneme-to-grapheme conversion for continuous, error-free phonemic strings; past work on isolated-word speech-to-text conversion; and more recent work on continuous-speech, speech-to-text conversion. Also demonstrated is the use of phone recognition for both language and speaker identification. The continuous speech-to-text conversion for French is based on a speaker-independent, vocabulary-independent recognizer. In this paper phone recognition and word recognition results are reported evaluating this recognizer on read speech taken from the BREF corpus. The recognizer was trained on over 4 hours of speech from 57 speakers, and tested on sentences from an independent set of 19 speakers. A phone accuracy of 78.7% was obtained using a set of 35 phones. The word accuracy was 88% for a 1139 word lexicon and 86% for a 2716 word lexicon, with a word pair grammar with respective perplexities of 100 and 160. Using a bigram grammar, word accuracies of 85.5% and 81.7% were obtained with 5 K and 20 K word vocabularies, with respective perplexities of 122 and 205.

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25

Duan, Dingbo, Guangyu Gao, Chi Harold Liu, and Jian Ma. "Automatic Person Identification in Camera Video by Motion Correlation." Journal of Sensors 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/838751.

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Person identification plays an important role in semantic analysis of video content. This paper presents a novel method to automatically label persons in video sequence captured from fixed camera. Instead of leveraging traditional face recognition approaches, we deal with the task of person identification by fusing information from motion sensor platforms, like smart phones, carried on human bodies and extracted from camera video. More specifically, a sequence of motion features extracted from camera video are compared with each of those collected from accelerometers of smart phones. When strong correlation is detected, identity information transmitted from the corresponding smart phone is used to identify the phone wearer. To test the feasibility and efficiency of the proposed method, extensive experiments are conducted which achieved impressive performance.

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26

Chen, Meng Di, Lu Ling Liu, and Bin Huang. "On Applications of Cell Phone Two-Dimensional Code in China." Advanced Materials Research 756-759 (September 2013): 922–26. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.922.

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Cell phone two-dimensional code is the product of the combination of the two-dimensional code technology and mobile terminal cell phone, applied to give full play to the superiority of the two-dimensional code recognition technology with the convenience of mobile cell phone use. With the upgrading of Chinese communication network, the popularity of smart cell phones as well as the change of people's lives concept in recent years, the range of applications of the two-dimensional code has been increasingly wider. The paper makes analyses on the classification application of the two-dimensional code in China and at the same time explores a number of constraints existing in its development cause in China, and then it puts forward development strategies of the cell phone two-dimensional code applications in Chinese market and provides a reference to its popularity and application.* Mengdi Chen is the first author; Luling Liu is the correspondence author; Bin Huang is the instructor.

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27

Guo, Ying, Qinghua Liu, Xianlei Ji, Shengli Wang, Mingyang Feng, and Yuxi Sun. "Multimode Pedestrian Dead Reckoning Gait Detection Algorithm Based on Identification of Pedestrian Phone Carrying Position." Mobile Information Systems 2019 (October 31, 2019): 1–14. http://dx.doi.org/10.1155/2019/4709501.

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Pedestrian dead reckoning (PDR) is an essential technology for positioning and navigation in complex indoor environments. In the process of PDR positioning and navigation using mobile phones, gait information acquired by inertial sensors under various carrying positions differs from noise contained in the heading information, resulting in excessive gait detection deviation and greatly reducing the positioning accuracy of PDR. Using data from mobile phone accelerometer and gyroscope signals, this paper examined various phone carrying positions and switching positions as the research objective and analysed the time domain characteristics of the three-axis accelerometer and gyroscope signals. A principal component analysis algorithm was used to reduce the dimension of the extracted multidimensional gait feature, and the extracted features were random forest modelled to distinguish the phone carrying positions. The results show that the step detection and distance estimation accuracy in the gait detection process greatly improved after recognition of the phone carrying position, which enhanced the robustness of the PDR algorithm.

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28

Feng, HUANG, and DENG Yupeng. "Design of iris recognition lens of mobile phone." Journal of Applied Optics 41, no. 1 (2020): 37–42. http://dx.doi.org/10.5768/jao202041.0101006.

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29

Kam, Anna Chi Shan, John Ka Keung Sung, Tan Lee, Terence Ka Cheong Wong, and Andrew van Hasselt. "Improving Mobile Phone Speech Recognition by Personalized Amplification." Ear and Hearing 38, no. 2 (2017): e85-e92. http://dx.doi.org/10.1097/aud.0000000000000371.

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30

Melnar, Lynette, and James Talley. "Phone inventory optimization for multilingual automatic speech recognition." Journal of the Acoustical Society of America 112, no. 5 (November 2002): 2305. http://dx.doi.org/10.1121/1.4779285.

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31

Mogi, Reona, and Hiroyuki Kasai. "Recognition of environmental sound recorded by mobile phone." ACM SIGMOBILE Mobile Computing and Communications Review 16, no. 4 (February 4, 2013): 8–9. http://dx.doi.org/10.1145/2436196.2436201.

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32

Picone, J., G. R. Doddington, and D. S. Pallett. "Phone-mediated word alignment for speech recognition evaluation." IEEE Transactions on Acoustics, Speech, and Signal Processing 38, no. 3 (March 1990): 559–62. http://dx.doi.org/10.1109/29.106877.

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Lee, K. F., and H. W. Hon. "Speaker-independent phone recognition using hidden Markov models." IEEE Transactions on Acoustics, Speech, and Signal Processing 37, no. 11 (1989): 1641–48. http://dx.doi.org/10.1109/29.46546.

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34

Knill, Katherine Mary. "Word spotting using both filler and phone recognition." Journal of the Acoustical Society of America 108, no. 1 (2000): 23. http://dx.doi.org/10.1121/1.429523.

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Manjunath, K. E., and K. Sreenivasa Rao. "Improvement of Phone Recognition Accuracy Using Articulatory Features." Circuits, Systems, and Signal Processing 37, no. 2 (May 8, 2017): 704–28. http://dx.doi.org/10.1007/s00034-017-0568-8.

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Ketabdar, Hamed, and Hervé Bourlard. "Enhanced Phone Posteriors for Improving Speech Recognition Systems." IEEE Transactions on Audio, Speech, and Language Processing 18, no. 6 (August 2010): 1094–106. http://dx.doi.org/10.1109/tasl.2009.2023162.

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37

T.Siva, K. Swathi,. "Face recognition technique for door phone embedded system." IOSR Journal of Electronics and Communication Engineering 8, no. 4 (2013): 19–22. http://dx.doi.org/10.9790/2834-0841922.

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Salamea, C. R., L. S. D'Haro, and R. Cordoba. "Language Recognition using Neural Phone Embeddings and RNNLMs." IEEE Latin America Transactions 16, no. 7 (July 2018): 2033–39. http://dx.doi.org/10.1109/tla.2018.8447373.

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39

Joshi, Himanshu, Paul S. Rosenbloom, and Volkan Ustun. "Continuous phone recognition in the Sigma cognitive architecture." Biologically Inspired Cognitive Architectures 18 (October 2016): 23–32. http://dx.doi.org/10.1016/j.bica.2016.09.001.

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40

Liu, Da-rong, Po-chun Hsu, Yi-chen Chen, Sung-feng Huang, Shun-po Chuang, Da-yi Wu, and Hung-yi Lee. "Learning Phone Recognition From Unpaired Audio and Phone Sequences Based on Generative Adversarial Network." IEEE/ACM Transactions on Audio, Speech, and Language Processing 30 (2022): 230–43. http://dx.doi.org/10.1109/taslp.2021.3138720.

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41

Al-Tamimi, Layth Ali Hammadi, Abbas Alwan Sharif, and Murtadha Mohammed Shani. "Recognition Of Revenue In Mobile Phone Companies Under IFRS 15." Restaurant Business 118, no. 10 (October 18, 2019): 181–96. http://dx.doi.org/10.26643/rb.v118i10.9316.

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The aim of this research is to find out the adequacy and appropriateness of revenue recognition procedures in mobile phone companies and to know how well they comply with international financial reporting standards. The most important conclusion reached by the researcher is the lack of experience and know-how in the accounting and administrative staff working in most mobile phone companies. The most important recommendations of the research are the need to provide an efficient accounting and administrative staff with sufficient experience and know-how in the methods of recognizing revenues generated by mobile phone companies.

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Jung, Eunhwa, and Kyungho Hong. "Biometric verification based on facial profile images for mobile security." Journal of Systems and Information Technology 17, no. 1 (March 9, 2015): 91–100. http://dx.doi.org/10.1108/jsit-03-2014-0020.

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Purpose – This study aims at a biometric verification based on facial profile images for mobile security. The modern technology of mobile Internet devices and smart phones such as the iPhone series and Galaxy phone series has revealed the development of information technology of input and output devices as high-definition multimedia interface. The development of information technology requires novel biometric verification for personal identification or authentication in mobile security, especially in Internet banking and mobile Internet access. Our study deals with a biometric verification based on facial profile images for mobile security. Design/methodology/approach – The product of cellphones with built-in cameras gives us the opportunity of the biometric verification to recognize faces, fingerprints and biological features without any other special devices. Our study focuses on recognizing the left and right facial profile images as well as the front facial images as a biometric verification of personal identification and authentication for mobile security, which can be captured by smart phone devices such as iPhone 4 and Galaxy S2. Findings – As the recognition technique of the facial profile images for a biometric verification in mobile security is a very simple, relatively easy to use and inexpensive, it can be easily applied to personal mobile phone identification and authentication instead of passwords, keys or other methods. The biometric system can also be used as one of multiple verification techniques for personal recognition in a multimodal biometric system. Our experimental data are taken from persons of all ages, ranging from children to senior citizens. Originality/value – As the recognition technique of the facial profile images for a biometric verification in mobile security is very simple, relatively easy to use and inexpensive, it can be easily applied to personal mobile phone identification and authentication instead of passwords, keys or other methods. The biometric system can also be used as one of multiple verification techniques for personal recognition in a multimodal biometric system. Our experimental data are taken from persons of all ages, ranging from children to senior citizens.

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Upadhyay, Anurag, and Chitranjanjit Kaur. "Enhancement of Speech Recognition System by neural network approaches of Clustering." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 6, no. 1 (May 30, 2013): 266–71. http://dx.doi.org/10.24297/ijct.v6i1.4456.

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This paper addresses the problem of speech recognition to identify various modes of speech data. Speaker sounds are the acoustic sounds of speech. Statistical models of speech have been widely used for speech recognition under neural networks. In paper we propose and try to justify a new model in which speech co articulation the effect of phonetic context on speech sound is modeled explicitly under a statistical framework. We study speech phone recognition by recurrent neural networks and SOUL Neural Networks. A general framework for recurrent neural networks and considerations for network training are discussed in detail. SOUL NN clustering the large vocabulary that compresses huge data sets of speech. This project also different Indian languages utter by different speakers in different modes such as aggressive, happy, sad, and angry. Many alternative energy measures and training methods are proposed and implemented. A speaker independent phone recognition rate of 82% with 25% frame error rate has been achieved on the neural data base. Neural speech recognition experiments on the NTIMIT database result in a phone recognition rate of 68% correct. The research results in this thesis are competitive with the best results reported in the literature.Â

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Mackersie, Carol L., Yingyong Qi, Arthur Boothroyd, and Nicole Conrad. "Evaluation of Cellular Phone Technology with Digital Hearing Aid Features: Effects of Encoding and Individualized Amplification." Journal of the American Academy of Audiology 20, no. 02 (February 2009): 109–18. http://dx.doi.org/10.3766/jaaa.20.2.4.

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Purpose: To compare multichannel amplification within a cellular phone system to a standard cellular phone response. Research Design: Three cellular phone speech-encoding strategies were evaluated: a narrow-band (3.5 kHz upper cutoff) enhanced variable-rate coder (EVRC), a narrow-band selectable mode vocoder (SMV), and a wide-band SMV (7.5 kHz cutoff). Because the SMV encoding strategies are not yet available on phones, the processing was simulated using a computer. Individualized-amplification settings were created using NAL-NL1 (National Acoustic Laboratories—Non-linear 1) targets. Overall gain was set at preferred listening levels for both the individualized-amplification setting and the standard cellular phone setting for each of the three encoders. Phoneme-recognition scores and subjective ratings (listening effort, overall quality) were obtained in quiet and in noise. Stimuli were played from loudspeakers in one room, picked up by a microphone connected to a (transmitting) computer, and sent over the Internet to a receiving computer in an adjacent room, where the signal was amplified and delivered monaurally. Study Sample: Fourteen participants with hearing loss. Results: Phoneme scores and subjective ratings were significantly higher for the individualized-amplification setting than for the standard setting in both quiet and noise. There were no significant differences among the cellular phone encoding strategies for any measure.

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Wang, Lei, Xiang Zhang, Yuanshuang Jiang, Yong Zhang, Chenren Xu, Ruiyang Gao, and Daqing Zhang. "Watching Your Phone's Back." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 5, no. 2 (June 23, 2021): 1–26. http://dx.doi.org/10.1145/3463522.

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Gesture recognition on the back surface of mobile phone, not limited to the touch screen, is an enabling Human-Computer Interaction (HCI) mechanism which enriches the user interaction experiences. However, there are two main limitations in the existing Back-of-Device (BoD) gesture recognition systems. They can only handle coarse-grained gesture recognition such as tap detection and cannot avoid the air-borne propagation suffering from the interference in the air. In this paper, we propose StruGesture, a fine-grained gesture recognition system using the back of mobile phones with ultrasonic signals. The key technique is to use the structure-borne sounds (i.e., sound propagation via structure of the device) to recognize sliding gestures on the back of mobile phones. StruGesture can fully extract the structure-borne component from the hybrid Channel Impulse Response (CIR) based on Peak Selection Algorithm. We develop a deep adversarial learning architecture to learn the gesture-specific representation for robust and effective recognition. Extensive experiments are designed to evaluate the robustness over nine deployment scenarios. The results show that StruGesture outperforms the competitive state-of-the-art classifiers by achieving an average recognition accuracy of 99.5% over 10 gestures.

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Kim, Jin-Ho. "Automatic Recognition of Bank Security Card Using Smart Phone." Journal of the Korea Contents Association 16, no. 12 (December 28, 2016): 19–26. http://dx.doi.org/10.5392/jkca.2016.16.12.019.

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Vishwasrao, Sahil. "Object Recognition for Visually Impaired Persons Using Smart phone." International Journal for Research in Applied Science and Engineering Technology 7, no. 5 (May 31, 2019): 2065–68. http://dx.doi.org/10.22214/ijraset.2019.5345.

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Sivaraman, Ganesh, Vikramjit Mitra, Hosung Nam, Elliot Saltzman, and Carol Espy-Wilson. "Augmenting acoustic phonetics with articulatory features for phone recognition." Journal of the Acoustical Society of America 137, no. 4 (April 2015): 2302. http://dx.doi.org/10.1121/1.4920401.

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Singer, Harald, and Shigeki Sagayama. "Pitch dependent phone modelling for HMM-based speech recognition." Journal of the Acoustical Society of Japan (E) 15, no. 2 (1994): 77–86. http://dx.doi.org/10.1250/ast.15.77.

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Chetty, Girija, Matthew White, and Farnaz Akther. "Smart Phone Based Data Mining for Human Activity Recognition." Procedia Computer Science 46 (2015): 1181–87. http://dx.doi.org/10.1016/j.procs.2015.01.031.

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