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209,882 results on '"Speech recognition"'

2. Detecting and Analyzing the Emotional Levels of a Person Through CBT Using MFCC and Lexicon-Based Approach

Author

Yasaswini, P., Sri Devendra, K., Sai Sathwik, G., Uday Kiran, T., Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Gunjan, Vinit Kumar, editor, and Zurada, Jacek M., editor

Published
2024
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4. PenBOT—Make Transcribing Easy with an AI Scribe

Author

Sai Krishna, N. M., Priyakanth, R., Srinika Sharma, C., Aalla, Chithra Bhanu, Kolluru, Sudiksha, Yalavarthy, Grahya, Sai Uma Maheswari, K., Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Tan, Kay Chen, Series Editor, Chakravarthy, V. V. S. S. S, editor, Bhateja, Vikrant, editor, Anguera, Jaume, editor, Urooj, Shabana, editor, and Ghosh, Anumoy, editor

Published
2024
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5. Speech Emotion Recognition Using Deep Learning Algorithm on RAVDESS Dataset

Author

Barsainyan, Nishant, Singh, Dileep Kumar, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Tan, Kay Chen, Series Editor, George, V. I., editor, Santhosh, K. V., editor, and Lakshminarayanan, Samavedham, editor

Published
2024
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7. Multilingual Speech Recognition: An In-Depth Review of Applications, Challenges, and Future Directions

Author

Jani, Mayur M., Panchal, Sandip R., Patel, Hemant H., Raiyani, Ashwin, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Sharma, Harish, editor, Shrivastava, Vivek, editor, Tripathi, Ashish Kumar, editor, and Wang, Lipo, editor

Published
2024
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8. Research and Design of a Smart Home Control System Based on Wireless WiFi Technology

Author

Sun, Hanbing, Lu, Biao, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Tan, Kay Chen, Series Editor, Wang, Wei, editor, Liu, Xin, editor, Na, Zhenyu, editor, and Zhang, Baoju, editor

Published
2024
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10. An Open-Source Voice Command-Based Human-Computer Interaction System Using Speech Recognition Platforms

Author

Fuad, Adnan Mahmud, Ahmed, Sheikh Jahan, Anannya, Nusrat Jahan, Mridha, M. F., Nur, Kamruddin, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Arefin, Mohammad Shamsul, editor, Kaiser, M. Shamim, editor, Bhuiyan, Touhid, editor, Dey, Nilanjan, editor, and Mahmud, Mufti, editor

Published
2024
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13. Time-Warp-Invariant Processing with Multi-spike Learning

Author

Zhou, Xiaohan, Liu, Yuzhe, Sun, Wei, Yu, Qiang, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Luo, Biao, editor, Cheng, Long, editor, Wu, Zheng-Guang, editor, Li, Hongyi, editor, and Li, Chaojie, editor

Published
2024
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14. Speech Command Recognition Algorithm Based on Improved MFCC Features

Author

Song, Ziyi, Ma, Lin, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Tan, Kay Chen, Series Editor, Wang, Wei, editor, Liu, Xin, editor, Na, Zhenyu, editor, and Zhang, Baoju, editor

Published
2024
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17. An Investigational Analysis of Automatic Speech Recognition on Deep Neural Networks and Gated Recurrent Unit Model

Author

Soundarya, M., Anusuya, S., Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Das, Swagatam, editor, Saha, Snehanshu, editor, Coello Coello, Carlos A., editor, and Bansal, Jagdish C., editor

Published
2024
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19. Speech Recognition Using HMM-CNN

Author

Santos, Lyndainês, de Araújo Moreira, Nícolas, Sampaio, Robson, Lima, Raizielle, Oliveira, Francisco Carlos Mattos Brito, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Rocha, Alvaro, editor, Adeli, Hojjat, editor, Dzemyda, Gintautas, editor, Moreira, Fernando, editor, and Colla, Valentina, editor

Published
2024
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20. Attention-Based Deep Convolutional Network for Speech Recognition Under Multi-scene Noise Environment

Author

Yang, Chuanwu, Ye, Shuo, Lin, Zhishu, Peng, Qinmu, Xu, Jiamiao, Yuan, Peipei, Wang, Yuetian, You, Xinge, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Luo, Biao, editor, Cheng, Long, editor, Wu, Zheng-Guang, editor, Li, Hongyi, editor, and Li, Chaojie, editor

Published
2024
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21. Joint Speech and Noise Estimation Using SNR-Adaptive Target Learning for Deep-Learning-Based Speech Enhancement

Author

Li, Xiaoran, Guo, Zilu, Du, Jun, Lee, Chin-Hui, Gao, Yu, Zhang, Wenbin, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Jia, Jia, editor, Ling, Zhenhua, editor, Chen, Xie, editor, Li, Ya, editor, and Zhang, Zixing, editor

Published
2024
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22. Semi-End-to-End Nested Named Entity Recognition from Speech

Author

Zhang, Min, Qiao, XiaoSong, Zhao, Yanqing, Su, Chang, Li, Yuang, Li, Yinglu, Piao, Mengyao, Peng, Song, Tao, Shimin, Yang, Hao, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Jia, Jia, editor, Ling, Zhenhua, editor, Chen, Xie, editor, Li, Ya, editor, and Zhang, Zixing, editor

Published
2024
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24. TST: Time-Sparse Transducer for Automatic Speech Recognition

Author

Zhang, Xiaohui, Liang, Mangui, Tian, Zhengkun, Yi, Jiangyan, Tao, Jianhua, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Fang, Lu, editor, Pei, Jian, editor, Zhai, Guangtao, editor, and Wang, Ruiping, editor

Published
2024
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25. A Survey of Speech Recognition for People with Cerebral Palsy

Author

Wu, Yu-Ru, Hung, Jason C., Chang, Jia-Wei, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Tan, Kay Chen, Series Editor, Hung, Jason C., editor, Yen, Neil, editor, and Chang, Jia-Wei, editor

Published
2024
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26. A Novel Approach to Video Summarization Using AI-GPT and Speech Recognition

Author

Prabhu, B. P. Aniruddha, Sharma, Tushar, Dani, Rakesh, Prasad, M. S. Guru, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Nanda, Satyasai Jagannath, editor, Yadav, Rajendra Prasad, editor, Gandomi, Amir H., editor, and Saraswat, Mukesh, editor

Published
2024
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29. Voice Commands with Virtual Assistant in Mixed Reality Telepresence

Author

Ishigaki, Shafina Abd Karim, Ismail, Ajune Wanis, Halim, Nur Ameerah Abdul, Suaib, Norhaida Mohd, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Ortis, Alessandro, editor, Hameed, Alaa Ali, editor, and Jamil, Akhtar, editor

Published
2024
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31. Agricultural Robotic System: The Automation of Detection and Speech Control

Author

Wenkai, Yang, Ruihang, Ji, Yiran, Yue, Zhonghan, Gu, Wanyang, Shu, Shuzhi, Sam Ge, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Ali, Abdulaziz Al, editor, Cabibihan, John-John, editor, Meskin, Nader, editor, Rossi, Silvia, editor, Jiang, Wanyue, editor, He, Hongsheng, editor, and Ge, Shuzhi Sam, editor

Published
2024
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32. Speech Therapy Supported by AI and Smart Assistants

Author

Calvano, Miriana, Curci, Antonio, Pagano, Alessandro, Piccinno, Antonio, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Kadgien, Regine, editor, Jedlitschka, Andreas, editor, Janes, Andrea, editor, Lenarduzzi, Valentina, editor, and Li, Xiaozhou, editor

Published
2024
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35. Development and Implementation of Voice-Controlled 3D Movement of Robotic Arm Based on Embedded System

Author

Sarkar, Alakesh, Bhowal, Partha, Hazarika, Nityananda, Roy, Ram Kishore, Singh, Hidam Kumarjit, Bezboruah, Tulshi, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Tan, Kay Chen, Series Editor, Deka, Jatindra Kumar, editor, Robi, P. S., editor, and Sharma, Bobby, editor

Published
2024
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36. PETSAI: Physical Education Teaching Support with Artificial Intelligence

Author

Ma, Thanh, Huynh, Thanh-Nhan, Tran, Viet-Chau, Phan, Bich-Chung, Pham, Nguyen-Khang, Do, Thanh-Nghi, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Thai-Nghe, Nguyen, editor, Do, Thanh-Nghi, editor, and Haddawy, Peter, editor

Published
2024
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39. Dysarthric Speech Characterization and Classification Based on Affinity Propagation

Author

Bharti, Komal, Agri, Sandeep, Das, Pradip K., Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Tan, Kay Chen, Series Editor, Swain, Bibhu Prasad, editor, and Dixit, Uday Shanker, editor

Published
2024
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40. Design and Evaluation of Speech Processing Systems for Meetei/Meitei Mayek

Author

Pangsatabam, Hoomexsun, Chanu, Yambem Jina, Singh, Naorem Karline, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Tan, Kay Chen, Series Editor, Swain, Bibhu Prasad, editor, and Dixit, Uday Shanker, editor

Published
2024
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41. Telephone follow‐up based on artificial intelligence technology among hypertension patients: Reliability study.

Author

Wang, Siyuan, Shi, Yan, Sui, Mengyun, Shen, Jing, Chen, Chen, Zhang, Lin, Zhang, Xin, Ren, Dongsheng, Wang, Yuheng, Yang, Qinping, Gao, Junling, and Cheng, Minna

Abstract

Artificial intelligence (AI) telephone is reliable for the follow‐up and management of hypertensives. It takes less time and is equivalent to manual follow‐up to a high degree. We conducted a reliability study to evaluate the efficiency of AI telephone follow‐up in the management of hypertension. During May 18 and June 30, 2020, 350 hypertensives managed by the Pengpu Community Health Service Center in Shanghai were recruited for follow‐up, once by AI and once by a human. The second follow‐up was conducted within 3–7 days (mean 5.5 days). The mean length time of two calls were compared by paired t‐test, and Cohen's Kappa coefficient was used to evaluate the reliability of the results between the two follow‐up visits. The mean length time of AI calls was shorter (4.15 min) than that of manual calls (5.24 min, P < .001). The answers related to the symptoms showed moderate to substantial consistency (κ:.465–.624, P < .001), and those related to the complications showed fair consistency (κ:.349, P < .001). In terms of lifestyle, the answer related to smoking showed a very high consistency (κ:.915, P < .001), while those addressing salt consumption, alcohol consumption, and exercise showed moderate to substantial consistency (κ:.402–.645, P < .001). There was moderate consistency in regular usage of medication (κ:.484, P < .001). [ABSTRACT FROM AUTHOR]

Published
2024
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42. A Study on Speech Recognition by a Neural Network Based on English Speech Feature Parameters.

Author

Mao, Congmin and Liu, Sujing

Subjects
*SPEECH perception, *ARTIFICIAL neural networks, *SPEECH, *ENGLISH language, *RECURRENT neural networks, *AUTOMATIC speech recognition, *FILTER banks
Abstract

In this study, from the perspective of English speech feature parameters, two feature parameters, the mel-frequency cepstral coefficient (MFCC) and filter bank (Fbank), were selected to identify English speech. The algorithms used for recognition employed the classical back-propagation neural network (BPNN), recurrent neural network (RNN), and long short-term memory (LSTM) that were obtained by improving RNN. The three recognition algorithms were compared in the experiments, and the effects of the two feature parameters on the performance of the recognition algorithms were also compared. The LSTM model had the best identification performance among the three neural networks under different experimental environments; the neural network model using the MFCC feature parameter outperformed the neural network using the Fbank feature parameter; the LSTM model had the highest correct rate and the highest speed, while the RNN model ranked second, and the BPNN model ranked worst. The results confirm that the application of the LSTM model in combination with MFCC feature parameter extraction to English speech recognition can achieve higher speech recognition accuracy compared to other neural networks. [ABSTRACT FROM AUTHOR]

Published
2024
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43. Microwave Speech Recognizer Empowered by a Programmable Metasurface.

Author

Zhang, Hongrui, Ruan, Hengxin, Zhao, Hanting, Wang, Zhuo, Hu, Shengguo, Cui, Tie Jun, del Hougne, Philipp, and Li, Lianlin

Subjects
*SPEECH, *SPEECH perception, *MICROWAVES, *OPTICAL sensors, *DEGREES of freedom, *HUMAN voice, *AUTOMATIC speech recognition, *SPEECH synthesis, *MEDICAL masks
Abstract

Speech recognition becomes increasingly important in the modern society, especially for human–machine interactions, but its deployment is still severely thwarted by the struggle of machines to recognize voiced commands in challenging real‐life settings: oftentimes, ambient noise drowns the acoustic sound signals, and walls, face masks or other obstacles hide the mouth motion from optical sensors. To address these formidable challenges, an experimental prototype of a microwave speech recognizer empowered by programmable metasurface is presented here that can remotely recognize human voice commands and speaker identities even in noisy environments and if the speaker's mouth is hidden behind a wall or face mask. The programmable metasurface is the pivotal hardware ingredient of the system because its large aperture and huge number of degrees of freedom allows the system to perform a complex sequence of sensing tasks, orchestrated by artificial‐intelligence tools. Relying solely on microwave data, the system avoids visual privacy infringements. The developed microwave speech recognizer can enable privacy‐respecting voice‐commanded human–machine interactions is experimentally demonstrated in many important but to‐date inaccessible application scenarios. The presented strategy will unlock new possibilities and have expectations for future smart homes, ambient‐assisted health monitoring, as well as intelligent surveillance and security. [ABSTRACT FROM AUTHOR]

Published
2024
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44. Evaluation of the American English Matrix Test with Cochlear Implant Recipients.

Author

Potts, Lisa G., Olivo, Alison M., Reeder, Ruth M., and Firszt, Jill B.

Subjects
*COCHLEAR implants, *NOISE, *DATA analysis, *AUDIOMETRY, *INTELLIGIBILITY of speech, *DESCRIPTIVE statistics, *STATISTICS, *HEARING disorders, *SPEECH perception, *COMPARATIVE studies
Abstract

Currently, the variety of speech recognition tests used to evaluate cochlear implant (CI) recipients makes it difficult to compare results, especially across languages. The Matrix Test limits contextual cues and is available in multiple languages, including American English. The current study investigated test format and noise type for the American English Matrix Test (AMT) and compared results to AzBio sentence scores in adult CI recipients. Fifteen experienced CI recipients were administered the AMT in fixed- and adaptive-level formats and AzBio sentences in a fixed-level format. Testing in noise used the AMT-specific noise and 4-talker babble. Ceiling effects were present for all AMT fixed-level conditions and AzBio sentences in quiet. Group mean AzBio scores were poorer than AMT scores. Noise type affected performance regardless of format; 4-talker babble was more challenging. The limited number of word choices in each category likely aided listeners performance for the AMT compared to AzBio sentences. The use of the AMT in the designed adaptive-level format would allow effective evaluation and comparison of CI performance internationally. A test battery with the AMT may also benefit from including AzBio sentences in 4-talker babble to reflect performance during listening challenges. [ABSTRACT FROM AUTHOR]

Published
2024
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45. The design of intelligent fuzzy cognitive system of music emotion by product supply chain management.

Author

Li, Fanfan, Jiang, Rong, and Li, Jiabao

Subjects
*MUSIC & emotions, *SUPPLY chain management, *AUTOMATIC speech recognition, *EMOTION recognition, *FEATURE extraction, *FUZZY systems, *SPEECH synthesis, *K-nearest neighbor classification
Abstract

The classification of musical emotion is a challenging problem due to the subjectivity of human perception. In order to analyse the emotion contained in music from the perspective of product supply chain management, the psychological model and characteristics of music emotion are introduced. Additionally, the current product supply chain and the composition of the two contracts are analysed based on the characteristics of online music products. Finally, a speech emotion recognition model based on a Fuzzy Cognitive Map (FCM) is proposed. Two corpora containing five emotions (sadness, anger, happiness, surprise, and boredom) are selected to test the performance and characteristics of the network. In order to evaluate the system performance, different acoustic features are extracted, including prosodic features, formant features, marginal spectral features, and Mel‐Frequency Cepstral Coefficients (MFCC). The results show that the average recognition rate of MFCC features on the Berlin emotional speech database is 70.36%, which is 0.75% higher than the second‐highest edge spectrum features and achieves better results. Meanwhile, on this database, the Musical Emotion Recognition System based on FCM (MERS‐FCM) is improved by 20.36%, 7.34%, and 4.12% compared with Back Propagation, K‐nearest neighbour, and support vector machine, respectively, which shows that MERS‐FCM has better performance. The research of the music emotion recognition system based on FCM can provide more reference paths for the use and development of music emotion information. [ABSTRACT FROM AUTHOR]

Published
2024
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46. Deep Learning Approaches for English-Marathi CodeSwitched Detection.

Author

Bhimanwar, Shreyash, Viralekar, Onkar, Anturkar, Koustubh, and Kulkarni, Ashwini

Subjects
ARTIFICIAL intelligence, SOFTWARE development tools, CODE switching (Linguistics), ORAL communication, CALL centers, DEEP learning
Abstract

During a conversation, speakers in multilingual societies frequently switch between two or more spoken languages. A linguistic action known as "code-switching" particularly alters or merges two or more languages. The development of software or tools for detecting code-switching has received very little attention. This paper proposes a Deep Learning based methods for detecting code-switched English-Marathi data. These suggested methods can be applied to various applications, including phone call merging, Intelligent AI assistants, Intelligent travelling systems to assist travellers in navigation and reservations, call centres to handle customer service issues, etc. To create a system for code switch detection, our study demonstrates a detailed analysis of extracting several audio features such as the Mel-Spectrogram, Mel-frequency Cepstral Coefficient (MFCC), and Perceptual Linear Predictive coefficients (PLP). Our team's EnglishMarathi code-switched dataset served as the testing ground for our methodologies. Our model's accuracy was 92.99%, with 40 MFCC coefficients having energy coefficient serving as the zeroth coefficient. [ABSTRACT FROM AUTHOR]

Published
2024
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47. Co-learning synaptic delays, weights and adaptation in spiking neural networks.

Author

Deckers, Lucas, Van Damme, Laurens, Van Leekwijck, Werner, Ing Jyh Tsang, and Latré, Steven

Subjects
ARTIFICIAL neural networks, NEUROPLASTICITY, SPEECH perception, ACTION potentials, ELECTRONIC data processing
Abstract

Spiking neural network (SNN) distinguish themselves from artificial neural network (ANN) because of their inherent temporal processing and spike-based computations, enabling a power-efficient implementation in neuromorphic hardware. In this study, we demonstrate that data processing with spiking neurons can be enhanced by co-learning the synaptic weights with two other biologically inspired neuronal features: (1) a set of parameters describing neuronal adaptation processes and (2) synaptic propagation delays. The former allows a spiking neuron to learn how to specifically react to incoming spikes based on its past. The trained adaptation parameters result in neuronal heterogeneity, which leads to a greater variety in available spike patterns and is also found in the brain. The latter enables to learn to explicitly correlate spike trains that are temporally distanced. Synaptic delays reflect the time an action potential requires to travel from one neuron to another. We show that each of the co-learned features separately leads to an improvement over the baseline SNN and that the combination of both leads to state-of-the-art SNN results on all speech recognition datasets investigated with a simple 2-hidden layer feed-forward network. Our SNN outperforms the benchmark ANN on the neuromorphic datasets (Spiking Heidelberg Digits and Spiking Speech Commands), even with fewer trainable parameters. On the 35-class Google Speech Commands dataset, our SNN also outperforms a GRU of similar size. Our study presents brain-inspired improvements in SNN that enable them to excel over an equivalent ANN of similar size on tasks with rich temporal dynamics. [ABSTRACT FROM AUTHOR]

Published
2024
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48. 基于 Transformer的多编码器端到端语音识别.

Author

鹿江飞,孙占全

Abstract

local feature information at shallow layers. To solve this problem, this study proposes a method using multiple encoders to improve the ability of speech feature extraction. An additional convolutional encoder branch is added to strengthen the capture of local feature information, make up for the neglect of local feature information in shallow Transformer, and effectively realize the integration of global and local dependencies of andio feature se quences. In other words, a multi-encoder model hased on Transformer is proposed. Experiments on the open source Chinese Mandarin data set Aishell 1 show that without an external language model, the proposed Transformer-based multi-encoder model has a relative reduction of 4.00% in character error rate when compared with the Transformer model. On the internal non public Shanghainese dialect data set, the performance improve ment of the proposed model is more obvious, and the character error rate is reduced by 48.24% from 19.92% to 10. 31%. [ABSTRACT FROM AUTHOR]

Published
2024
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49. Current advances and algorithmic solutions in speech generation.

Author

Oralbekova, Dina, Mamyrbayev, Orken, Kassymova, Dinara, and Othman, Mohamed

Subjects
*DEEP learning, *NATURAL language processing, *SPEECH, *SPEECH synthesis, *HIDDEN Markov models, *ACOUSTIC models
Abstract

Currently, Text-to-Speech (TTS) technology, aimed at reproducing a natural human voice from text, is gaining increasing demand in natural language processing. Key criteria for evaluating the quality of synthesized sound include its clarity and naturalness, which largely depend on the accurate modeling of intonations using the acoustic model in the speech generation system. This paper presents fundamental methods such as concatenative and parametric speech synthesis, speech synthesis based on hidden Markov models, and deep learning approaches like end-to-end models for building the acoustic model. The article discusses metrics for evaluating the quality of synthesized voice. Brief overviews of modern text-to-speech architectures, such as WaveNet, Tacotron, and Deep Voice, applying deep learning and demonstrating quality ratings close to professionally recorded speech, are also provided. [ABSTRACT FROM AUTHOR]

Published
2024
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50. Detection and Recognition of Voice Commands by a Distributed Acoustic Sensor Based on Phase-Sensitive OTDR in the Smart Home Concept.

Author

Gritsenko, Tatyana V., Orlova, Maria V., Zhirnov, Andrey A., Konstantinov, Yuri A., Turov, Artem T., Barkov, Fedor L., Khan, Roman I., Koshelev, Kirill I., Svelto, Cesare, and Pnev, Alexey B.

Subjects
*DISTRIBUTED sensors, *HOME (The concept), *SMART homes, *SPEECH perception, *INTELLIGENT sensors, *SIGNAL processing
Abstract

In recent years, attention to the realization of a distributed fiber-optic microphone for the detection and recognition of the human voice has increased, whereby the most popular schemes are based on φ-OTDR. Many issues related to the selection of optimal system parameters and the recognition of registered signals, however, are still unresolved. In this research, we conducted theoretical studies of these issues based on the φ-OTDR mathematical model and verified them with experiments. We designed an algorithm for fiber sensor signal processing, applied a testing kit, and designed a method for the quantitative evaluation of our obtained results. We also proposed a new setup model for lab tests of φ-OTDR single coordinate sensors, which allows for the quick variation of their parameters. As a result, it was possible to define requirements for the best quality of speech recognition; estimation using the percentage of recognized words yielded a value of 96.3%, and estimation with Levenshtein distance provided a value of 15. [ABSTRACT FROM AUTHOR]

Published
2024
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