Protecting World Leader Using Facial Speaking Pattern Against Deepfakes

Συγγραφείς: Chu, B., You, W., Yang, Z., Zhou, L., Wang, R.

Πηγή: IEEE Signal Processing Letters IEEE Signal Process. Lett. Signal Processing Letters, IEEE. 29:2078-2082 2022

Machine Learning Based Automated Speech Dialog Analysis Of Autistic Children

Συγγραφείς: Wijesinghe, Anjana, Samarasinghe, Pradeepa, Seneviratne, Sudarshi, Yogarajah, Pratheepan, Pulasinghe, Koliya

Πηγή: 2019 11th International Conference on Knowledge and Systems Engineering (KSE) Systems Engineering (KSE), 2019 11th International Conference on Knowledge and. :1-5 Oct, 2019

Relation: 2019 11th International Conference on Knowledge and Systems Engineering (KSE)

A Novel Framework for Enhancing Speech Pattern Recognition for Early Detection of Alzheimer's Disease Using machine learning Approach

Συγγραφείς: Rajashree Chakraborty

Πηγή: International Journal of Intelligent Systems and Applications in Engineering; Vol. 12 No. 22s (2024); 421-428

Θεματικοί όροι: Alzheimer's Disease, Early Detection, Speech Pattern Recognition, Support Vector Machines (SVM), Acoustic Analysis, Cognitive Impairment, Medical Diagnostics, Non-Invasive Diagnosis, Machine Learning, Healthcare Technology

Περιγραφή αρχείου: application/pdf

Σύνδεσμος πρόσβασης: https://www.ijisae.org/index.php/IJISAE/article/view/6480

ENHANCING SPEECH PATTERN RECOGNITION FOR EARLY DETECTION OF ALZHEIMER'S DISEASE USING CNN-LSTM AND NATURAL LANGUAGE PROCESSING TECHNIQUES

Συγγραφείς: Sonal Modh Bhardwaj, Shimpy Harbhajanka Goyal, Anusha Jain, Priyanka Dhasal, Dr. Balraj Kumar, Dr. Surjeet, Jayesh Surana

Θεματικοί όροι: Alzheimer's Disease, Early Detection, Speech Pattern Recognition, Natural Language Processing (NLP), Deep Learning, Machine Learning, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Cognitive Impairment, Medical Diagnostics

Relation: 1462 2815; https://zenodo.org/records/12743471; oai:zenodo.org:12743471; https://doi.org/10.5281/zenodo.12743471

Διαθεσιμότητα: https://doi.org/10.5281/zenodo.12743471
https://zenodo.org/records/12743471

Word boundary detection for engineering applications

Συγγραφείς: Agaiby, Hany

Θεματικοί όροι: 621.3994, Speech pattern recognition, Coding, Transmission

Σύνδεσμος πρόσβασης: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.265933

Evaluation of neural learning in a MLP NN for an acoustic-to-articulatory mapping problem using different training pattern vector characteristics

Συγγραφείς: Altun, Halis

Θεματικοί όροι: 621.3994, Speech pattern recognition, Mapping

Σύνδεσμος πρόσβασης: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.263405

Una comparación para el reconocimiento de patrones del habla usando Modelos de Markov Oculto y Redes Neuronales en el idioma Español

Συγγραφείς: Camargo Abril, Gustavo Arnulfo

Συνεισφορές: Calderón Villanueva, Sergio Alejandro

Θεματικοί όροι: Reconocimiento de patrones del habla, Neural Networks, Markov processes, Speech Pattern Recognition, Neural networks (Computer science), ANALISIS DE SERIES DE TIEMPO, Time Delay Neural Networks, 006 - Métodos especiales de computación [000 - Ciencias de la computación, información y obras generales], ANALISIS DE ERROR (MATEMATICAS), Time-series analysis, Word Error Rate, REDES NEURALES (COMPUTADORES), Cepstral Coefficients, Modelos de Markov Ocultos, Redes Neuronales, Tasa de Error por Palabra, 519 - Probabilidades y matemáticas aplicadas [510 - Matemáticas], PROCESOS DE MARKOV, Coeficientes Cepstrales, Error analysis (mathematics), Hidden Markov Models, Redes Neuronales de Retardo Temporal

Περιγραφή αρχείου: 104 páginas; application/pdf

Σύνδεσμος πρόσβασης: https://repositorio.unal.edu.co/handle/unal/87165

Multi-modal prediction and modelling using artificial neural networks

Συγγραφείς: Lee, Gareth E.

Θεματικοί όροι: 621.3994, Speech pattern recognition

Σύνδεσμος πρόσβασης: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.293823

Una comparación para el reconocimiento de patrones del habla usando Modelos de Markov Oculto y Redes Neuronales en el idioma Español ; A comparison of speech pattern recognition using hidden Markov models and neural networks in spanish language

Συγγραφείς: Camargo Abril, Gustavo Arnulfo

Συνεισφορές: Calderón Villanueva, Sergio Alejandro

Θεματικοί όροι: 510 - Matemáticas::519 - Probabilidades y matemáticas aplicadas, 000 - Ciencias de la computación, información y obras generales::006 - Métodos especiales de computación, REDES NEURALES (COMPUTADORES), PROCESOS DE MARKOV, ANALISIS DE SERIES DE TIEMPO, ANALISIS DE ERROR (MATEMATICAS), Neural networks (Computer science), Markov processes, Time-series analysis, Error analysis (mathematics), Reconocimiento de patrones del habla, Modelos de Markov Ocultos, Redes Neuronales, Redes Neuronales de Retardo Temporal, Tasa de Error por Palabra, Coeficientes Cepstrales, Speech Pattern Recognition, Hidden Markov Models, Neural Networks, Time Delay Neural Networks, Word Error Rate, Cepstral Coefficients

Περιγραφή αρχείου: 104 páginas; application/pdf

Relation: Abdel-Hamid, O., A. rahman Mohamed, H. Jiang, L. Deng, G. Penn, and D. Yu (2014, Oct). Convolutional neural networks for speech recognition. IEEE/ACM Transactions on Audio, Speech, and Language Processing@(10).; Amodei, D., R. Anubhai, E. Battenberg, C. Case, J. Casper, B. Catanzaro, J. Chen, M. Chrzanowski, A. Coates, G. Diamos, E. Elsen, J. Engel, L. Fan, C. Fougner, T. Han, A. Hannun, B. Jun, P. LeGresley, L. Lin, S. Narang, A. Ng, S. Ozair, R. Prenger, J. Raiman, S. Satheesh, D. Seetapun, S. Sengupta, Y. Wang, Z. Wang, C. Wang, B. Xiao, D. Yogatama, J. Zhan, and Z. Zhu (2015). Deep speech 2: End-to-end speech recognition in english and mandarin. Technical report, Baidu Research – Silicon Valley AI Lab.; Blair, C. (1989). The sphinx speech recognition system. In International Conference on Acoustics, Speech, and Signal Processing, Glasgow, UK, pp. 445–448 vol.1.; Chamroukhi, F. and H. D. Nguyen (2019). Model-based clustering and classification of functional data. WIREs Data Mining and Knowledge Discovery; Chaudhary, K. (2020). Understanding audio data, fourier transform, fft and spectrogram features for a speech recognition system; Chen, R. and R. S. Tsay (2019). Nonlinear Time Series Analysis. Wiley Series in Probability and Statistics. Wiley.; Collobert, R., C. Puhrsch, and G. Synnaeve (2016). Wav2letter: An end-toend convnet-based speech recognition system. Technical report, Facebook AI Research.; Davis, K. H., R. Biddulph, and S. Balashek (1952). Automatic recognition of spoken digits. Technical report, Bell Telephone Laboratories, Inc., Murray Hill, New Jersey.; Fink, G. A. (2014). Markov Models for Pattern Recognition. Springer-Verlag London.; Goel, N. K. and R. A. Gopinath (2001). Multiple linear transforms. In IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings, Volume 1, Salt Lake City, UT, USA, pp. 481–484.; Graves, A., A. rahman Mohamed, and G. Hinton (2013). Speech recognition with deep recurrent neural networks. IEEE Transactions on Neural Networks and Learning Systems@(10), 6642–6651.; Gubian, M., F. Torreira, and L. Boves (2015). Using functional data analysis for investigating multidimensional dynamic phonetic contrasts. Journal of Phonetics 49, 16–40.; Gubian, M., F. Torreira, H. Strik, and L. Boves (2009, Sep). Functional data analysis as a tool for analyzing speech dynamics: A case study on the french word c’était. In Conference Paper.; He, Y., T. N. Sainath, R. Prabhavalkar, I. McGraw, R. Alvarez, D. Zhao, D. Rybach, A. Kannan, Y. Wu, R. Pang, et al. (2019). Streaming end-to-end speech recognition for mobile devices. In ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6381–6385. IEEE.; Hernández-Mena, C. D., I. V. Meza-Ruiz, and J. A. Herrera-Camacho (2017). Automatic speech recognizers for mexican spanish and its open resources. Journal of Applied Research and Technology.; Hinton, G., L. Deng, D. Yu, G. Dahl, A. rahman Mohamed, N. Jaitly, and Andrew (2012). Deep neural networks for acoustic modeling in speech recognition. IEEE Signal Processing Magazine.; Hoffmeister, B., G. Heigold, D. Rybach, R. Schlüter, and H. Ney (2012). Wfst enabled solutions to asr problems: Beyond hmm decoding. IEEE Transactions on Audio, Speech, and Language Processing@(2).; Jaitly, N. (2018). Natural language processing with deep learning cs224n/ling284: Lecture 12: End-to-end models for speech processing. Online. Available: https: // web. stanford. edu/ class/ archive/ cs/ cs224n/ cs224n. 1174/ lectures/ .; Kamath, U., J. Liu, and J. Whitaker (2019). Deep Learning for NLP and Speech Recognition. Springer Nature Switzerland AG; Katz, M., H.-G. Meier, H. Döljing, and D. Klakow (2002). Robustness of linear discriminant analysis in automatic speech recognition. In International Conference on Pattern Recognition, Volume 3, Quebec City, QC, Canada, pp. 371–374.; Kumar, A. and R. K. Aggarwal (2020). Hindi speech recognition using time delay neural network acoustic modeling with i-vector adaptation. Springer Science+Business Media, LLC, part of Springer Nature.; Lee, K. F., H. W. Hon, M. Y. Hwang, S. Mahajan, and R. Reddy (1997). Dragon–naturallyspeaking. Journal of Osteopathic Medicine 12, 711.; Li, J., V. Lavrukhin, B. Ginsburg, R. Leary, O. Kuchaiev, J. M. Cohen, H. Nguyen, and R. T. Gadde (2019). Jasper: An end-to-end convolutional neural acoustic model. arXiv preprint.; Liao, Y.-F. (2018). Formosa speech recognition challenge (fsw). National Taipei University of Technology. Available online: https://sites.google.com/ speech.ntut.edu.tw/fsw/home/challenge.; Liao, Y.-F., W.-H. Hsu, Y.-C. Lin, Y.-H. S. Chang, M. Pleva, J. Juhar, and G.-F. Deng (2018). Formosa speech recognition challenge 2018: Data, plan and baselines. In 11th International Symposium on Chinese Spoken Language Processing (ISCSLP), Taipei, Taiwan.; Liu, B., W. Zhang, X. Xu, and D. Chen (2019). Time delay recurrent neural network for speech recognition. In IOP Conference Series: Journal of Physics: Conference Series, Volume 1229.; Mohri, M., F. Pereira, and M. Riley (2008). Speech recognition with weighted finite-state transducers. In Springer Handbook on Speech Processing and Speech Communication. Springer.; Nayak, S., S. Sarkar, and K. Sengupta (2004, Dec). Modeling signs using functional data analysis. In Fourth Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP), pp. 64–69.; Peddinti, V., D. Povey, S. Pu, and S. Khudanpur (2015). A time delay neural network architecture for efficient modeling of long temporal contexts. Technical report, Center for Language and Speech Processing and Human Language Technology Center of Excellence, Johns Hopkins University, Baltimore, MD 21218, USA.; Pigoli, D., P. Z. Hadjipantelis, J. S. Coleman, and J. A. Aston (2017, May). The statistical analysis of acoustic phonetic data: Exploring differences between spoken romance languages. arXiv:1507.07587v2 [stat.AP]. arXiv:1507.07587v2.; Povey, D., V. Peddinti, D. Galvez, P. Ghahrmani, V. Manohar, X. Na, Y. Wang, and S. Khudanpur (2016). Purely sequence-trained neural networks for asr based on lattice-free mmi. In Proc. Interspeech 2016, pp. 2751–2755.; Rabiner, L. and B. Juang (1986). An introduction to hidden markov models. IEEE ASSP Magazine@(1), 4–16.; Rabiner, L. and B. H. Juang (1993). Fundamentals of Speech Recognition. Englewood Cliffs: Prentice Hall.; Rabiner, L. R. (1989). A tutorial on hidden markov models and selected applications in speech recognition. IEEE@(2), 257–286.; Radaković, M. (2021). Audio signal preparation process for deep learning application using python. In International Scientific Conference on Information Technology and Data Related Research.; Rao, K., H. sim Sak, and R. Prabhavalkar (2017). Exploring architectures, data and units for streaming end-to-end speech recognition with rnn-transducer. In IEEE Automatic Speech Recognition and Understanding Workshop (ASRU), pp. 193–199. IEEE.; Renals, S. (2019). Decoding, alignment, and wfsts. Automatic Speech Recognition ASR Lecture 10. Available online: https://www.inf.ed.ac.uk/ teaching/courses/asr/index-2019.html.; Renals, S., C. Case, J. Casper, B. Catanzaro, G. Diamos, E. Elsen, R. Prenger, S. Satheesh, S. Sengupta, A. Coates, and A. Y. Ng (2014). Deep speech: Scaling up end-to-end speech recognition. Technical report, Baidu Research – Silicon Valley AI Lab.; Renals, S. and H. Shimodaira (2019). Context-dependent phone models. Automatic Speech Recognition ASR Lecture 6. Available online: https://www. inf.ed.ac.uk/teaching/courses/asr/index-2019.html.; Rumelhart, D. E., G. E. Hinton, and R. J. Williams (1988). Learning representations by backpropagating errors. In MIT Press, pp. 696–699.; Wang, S., Z. Shang, G. Cao, and J. S. Liu (2021, Sep). Optimal classification for functional data. arXiv:2103.00569v2 [stat.ME].; Wang, Y., X. Deng, S. Pu, and Z. Huang (2017). Residual convolutional ctc networks for automatic speech recognition. arXiv preprint.; Yakowitz, S. J. (1970). Unsupervised learning and the identification of finite mixtures. IEEE Transactions on Information Theory@(3), 330–338.; https://repositorio.unal.edu.co/handle/unal/87165; Universidad Nacional de Colombia; Repositorio Institucional Universidad Nacional de Colombia; https://repositorio.unal.edu.co/

Διαθεσιμότητα: https://repositorio.unal.edu.co/handle/unal/87165
https://repositorio.unal.edu.co/

Highly Sensitive Electronic Skins

Συγγραφείς: Huang, Yun-Chiao

Θεματικοί όροι: Materials Science, Electrical engineering, Bioengineering, electronic skin, healthcare monitoring, microphone, pressure mapping, pressure sensor, speech pattern recognition

Περιγραφή αρχείου: application/pdf

Σύνδεσμος πρόσβασης: https://escholarship.org/uc/item/9hg6p41g
https://escholarship.org/content/qt9hg6p41g/qt9hg6p41g.pdf

Použití jednotřídních klasifikátorů pro diferenciální diagnostiku dysartrie ; Application of one-class classifiers in differential diagnosis of dysarthria

Συγγραφείς: Tran Duc Minh

Συνεισφορές: Hlavnička Jan, Lustyk Tomáš

Θεματικοί όροι: Rozpoznávání řečových vzorů,Jednotřídní klasifikátor,Vícetřídní klasifikátor,Parkinsonova nemoc,Huntingtonova nemoc,Hypokinetická dysartrie,Hyperkinetická dysartrie, Speech pattern recognition,One-class classifier,Multi-class classifier,Parkinson's disease,Huntington's disease,Hypokinetic dysarthria,Hyperkinetic dysarthria

Περιγραφή αρχείου: application/pdf; application/octet-stream

Relation: http://hdl.handle.net/10467/80674

Διαθεσιμότητα: http://hdl.handle.net/10467/80674

Automatická analýza poruch řeči u neurodegenerativních onemocnění ; Automated analysis of speech disorders in neurodegenerative diseases

Συγγραφείς: Hlavnička Jan

Συνεισφορές: Čmejla Roman, Jech Robert

Θεματικοί όροι: Poruchy řeči,Neurodegenerace,Parkinsonova nemoc,Porucha chování v REM spánku,Multisystémová atrofie,Progresivní supranukleární obrna,Huntingtonova nemoc,Cerebelární ataxie,Roztroušená skleróza,Dysartrie,Akustická analýza,Rozpoznávání řečových vzorů, Speech disorders,Neurodegeneration,Parkinson’s disease,Rapid eye movement sleep behavior disorder,Multiple system atrophy,Progressive supranuclear palsy,Huntington’s disease,Cerebellar ataxia,Multiple sclerosis,Dysarthria,Acoustic analysis,Speech pattern recognition

Περιγραφή αρχείου: application/pdf

Relation: http://hdl.handle.net/10467/79223

Διαθεσιμότητα: http://hdl.handle.net/10467/79223

Highly Sensitive Electronic Skins

Συγγραφείς: Huang, Yu, Huang, Yun-Chiao

Όροι ευρετηρίου: Materials Science, Electrical engineering, Bioengineering, electronic skin, healthcare monitoring, microphone, pressure mapping, pressure sensor, speech pattern recognition, publication

Σύνδεσμος: https://escholarship.org/uc/item/9hg6p41g
https://escholarship.org/content/qt9hg6p41g/qt9hg6p41g.pdf