{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T15:52:09Z","timestamp":1771602729522,"version":"3.50.1"},"reference-count":55,"publisher":"Springer Science and Business Media LLC","issue":"13","license":[{"start":{"date-parts":[[2022,3,12]],"date-time":"2022-03-12T00:00:00Z","timestamp":1647043200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,3,12]],"date-time":"2022-03-12T00:00:00Z","timestamp":1647043200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2022,10]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>This paper proposes a framework of applying only the EGG signal for speech synthesis in the limited categories of contents scenario. EGG is a sort of physiological signal which can reflect the trends of the vocal cord movement. Note that EGG\u2019s different acquisition method contrasted with speech signals, we exploit its application in speech synthesis under the following two scenarios. (1) To synthesize speeches under high noise circumstances, where clean speech signals are unavailable. (2) To enable dumb people who retain vocal cord vibration to speak again. Our study consists of two stages, EGG to text and text to speech. The first is a text content recognition model based on Bi-LSTM, which converts each EGG signal sample into the corresponding text with a limited class of contents. This model achieves 91.12% accuracy on the validation set in a 20-class content recognition experiment. Then the second step synthesizes speeches with the corresponding text and the EGG signal. Based on modified Tacotron-2, our model gains the Mel cepstral distortion (MCD) of 5.877 and the mean opinion score (MOS) of 3.87, which is comparable with the state-of-the-art performance and achieves an improvement by 0.42 and a relatively smaller model size than the origin Tacotron-2. Considering to introduce the characteristics of speakers contained in EGG to the final synthesized speech, we put forward a fine-grained fundamental frequency modification method, which adjusts the fundamental frequency according to EGG signals and achieves a lower MCD of 5.781 and a higher MOS of 3.94 than that without modification.<\/jats:p>","DOI":"10.1007\/s10489-021-03075-x","type":"journal-article","created":{"date-parts":[[2022,3,12]],"date-time":"2022-03-12T16:02:25Z","timestamp":1647100945000},"page":"15193-15209","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Limited text speech synthesis with electroglottograph based on Bi-LSTM and modified Tacotron-2"],"prefix":"10.1007","volume":"52","author":[{"given":"Lijiang","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Ren","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pengfei","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xia","family":"Mao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qi","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,3,12]]},"reference":[{"key":"3075_CR1","doi-asserted-by":"publisher","DOI":"10.1515\/9783110873429","author":"G Fant","year":"1971","unstructured":"Fant G (1971) Acoustic Theory of Speech Production. De Gruyter Mouton. https:\/\/doi.org\/10.1515\/9783110873429","journal-title":"De Gruyter Mouton"},{"key":"3075_CR2","doi-asserted-by":"crossref","unstructured":"Tronchin L, Kob M, Guarnaccia C (2018) Spatial information on voice generation from a multi-channel electroglottograph. Applied Sciences 8(9) https:\/\/doi.org\/10.3390\/app8091560","DOI":"10.3390\/app8091560"},{"key":"3075_CR3","doi-asserted-by":"crossref","unstructured":"Hussein H, Jokisch O (2007) Hybrid electroglottograph and speech signal based algorithm for pitch marking. In: INTERSPEECH 2007, 8th Annual Conference of the International Speech Communication Association, Antwerp, Belgium, August 27-31, 2007, ISCA, pp 1653\u20131656","DOI":"10.21437\/Interspeech.2007-460"},{"issue":"1","key":"3075_CR4","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1007\/s12070-010-0099-0","volume":"63","author":"N Paul","year":"2011","unstructured":"Paul N, Kumar S, Chatterjee I, Mukherjee B (2011) Electroglottographic parameterization of the effects of gender, vowel and phonatory registers on vocal fold vibratory patterns An indian perspective. Indian Journal of Otolaryngology and Head & Neck Surgery 63(1):27\u201331. https:\/\/doi.org\/10.1007\/s12070-010-0099-0","journal-title":"Indian Journal of Otolaryngology and Head & Neck Surgery"},{"key":"3075_CR5","doi-asserted-by":"crossref","unstructured":"Hui L, Ting LH, See SL, Chan PY (2015) Use of electroglottograph (egg) to find a relationship between pitch, emotion and personality. Procedia Manufacturing pp 1926\u20131931 https:\/\/doi.org\/10.1016\/j.promfg.2015.07.236","DOI":"10.1016\/j.promfg.2015.07.236"},{"key":"3075_CR6","doi-asserted-by":"crossref","unstructured":"Macerata A, Nacci A, Manti M, Cianchetti M, Matteucci J, Romeo SO, Fattori B, Berrettini S, Laschi C, Ursino F (2017) Evaluation of the electroglottographic signal variability by amplitude-speed combined analysis. Biomedical Signal Processing and Control pp 61\u201368 https:\/\/doi.org\/10.1016\/j.bspc.2016.10.003","DOI":"10.1016\/j.bspc.2016.10.003"},{"key":"3075_CR7","doi-asserted-by":"publisher","unstructured":"Chen L, Mao X, Wei P, Compare Angelo (2013) Speech emotional features extraction based on electroglottograph. Neural Computation 25:3294\u20133317. https:\/\/doi.org\/10.1162\/neco_a_00523","DOI":"10.1162\/neco_a_00523"},{"issue":"12","key":"3075_CR8","doi-asserted-by":"publisher","first-page":"2281","DOI":"10.1109\/TASLP.2017.2759002","volume":"25","author":"M Borsky","year":"2017","unstructured":"Borsky M, Mehta DD, Van Stan JH, Gudnason J (2017) Modal and nonmodal voice quality classification using acoustic and electroglottographic features. IEEE\/ACM Transactions on Audio, Speech, and Language Processing 25(12):2281\u20132291. https:\/\/doi.org\/10.1109\/TASLP.2017.2759002","journal-title":"IEEE\/ACM Transactions on Audio, Speech, and Language Processing"},{"key":"3075_CR9","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1016\/j.bspc.2017.03.007","volume":"36","author":"SB Sunil Kumar","year":"2017","unstructured":"Sunil Kumar SB, Mandal T, Sreenivasa Rao K (2017) Robust glottal activity detection using the phase of an electroglottographic signal. Biomedical Signal Processing and Control 36:27\u201338. https:\/\/doi.org\/10.1016\/j.bspc.2017.03.007","journal-title":"Biomedical Signal Processing and Control"},{"key":"3075_CR10","doi-asserted-by":"publisher","unstructured":"Liu D, Kankare E, Laukkanen AM, Alku P (2017) Comparison of parametrization methods of electroglottographic and inverse filtered acoustic speech pressure signals in distinguishing between phonation types. Biomedical Signal Processing and Control 36(Jul.):183\u2013193 https:\/\/doi.org\/10.1016\/j.bspc.2017.04.001","DOI":"10.1016\/j.bspc.2017.04.001"},{"key":"3075_CR11","doi-asserted-by":"publisher","first-page":"528","DOI":"10.1016\/j.bspc.2019.01.004","volume":"49","author":"J Lebacq","year":"2019","unstructured":"Lebacq J, Dejonckere PH (2019) The dynamics of vocal onset. Biomedical Signal Processing and Control 49:528\u2013539. https:\/\/doi.org\/10.1016\/j.bspc.2019.01.004","journal-title":"Biomedical Signal Processing and Control"},{"key":"3075_CR12","doi-asserted-by":"publisher","first-page":"102064","DOI":"10.1016\/j.bspc.2020.102064","volume":"62","author":"MBL Filipa","year":"2020","unstructured":"Filipa MBL, Ternstrm S (2020) Flow ball-assisted voice training Immediate effects on vocal fold contacting. Biomedical Signal Processing and Control 62:102064. https:\/\/doi.org\/10.1016\/j.bspc.2020.102064","journal-title":"Biomedical Signal Processing and Control"},{"key":"3075_CR13","doi-asserted-by":"publisher","unstructured":"Niimi Y (2002) A chinese text to speech system based on td-psola. In: IEEE Region 10 Conference on Computers https:\/\/doi.org\/10.1109\/tencon.2002.1181250","DOI":"10.1109\/tencon.2002.1181250"},{"issue":"2","key":"3075_CR14","doi-asserted-by":"publisher","first-page":"820","DOI":"10.1121\/1.398894","volume":"87","author":"Dennis H Klatt","year":"1990","unstructured":"Klatt Dennis H (1990) Analysis, synthesis, and perception of voice quality variations among female and male talkers. The Journal of the Acoustical Society of America 87(2):820\u2013857. https:\/\/doi.org\/10.1121\/1.398894","journal-title":"The Journal of the Acoustical Society of America"},{"key":"3075_CR15","doi-asserted-by":"publisher","DOI":"10.1109\/icassp.1982.1171649","author":"BS Atal","year":"1982","unstructured":"Atal BS (1982) A new model of lpc excitation for producing natural-sounding speech at low bit rates. Proc ICASSP. https:\/\/doi.org\/10.1109\/icassp.1982.1171649","journal-title":"Proc ICASSP"},{"key":"3075_CR16","doi-asserted-by":"publisher","first-page":"S35","DOI":"10.1121\/1.1995189","volume":"57","author":"F Itakura","year":"1975","unstructured":"Itakura F (1975) Line spectrum representation of linear predictive coefficients of speech signals. Journal of Acoustic Society of America 57:S35. https:\/\/doi.org\/10.1121\/1.1995189","journal-title":"Journal of Acoustic Society of America"},{"key":"3075_CR17","first-page":"153","volume":"02","author":"L Qingfeng","year":"1998","unstructured":"Qingfeng L, Renhua W (1998) A new speech synthesis method based on the lma vocal tract model. Chinese Journal of Acoustics 02:153\u2013162","journal-title":"Chinese Journal of Acoustics"},{"key":"3075_CR18","unstructured":"Sotelo J, Mehri S, Kumar K, Santos JF, Kastner K, Courville AC, Bengio Y (2017) Char2wav End-to-end speech synthesis. In: 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Workshop Track Proceedings, OpenReview.net"},{"key":"3075_CR19","doi-asserted-by":"crossref","unstructured":"Kawahara H (1999) Restructuring speech representations using a pitch-adaptive time-frequency smoothing and an instantaneous-frequency-based f0 extraction Possible role of a repetitive structure in sounds. Speech Communication 27. https:\/\/doi.org\/10.1016\/S0167-6393(98)00085-5","DOI":"10.1016\/S0167-6393(98)00085-5"},{"issue":"7","key":"3075_CR20","doi-asserted-by":"publisher","first-page":"1877","DOI":"10.1587\/transinf.2015edp7457","volume":"99","author":"M Morise","year":"2016","unstructured":"Morise M, Yokomori F, Ozawa K (2016) World A vocoder-based high-quality speech synthesis system for real-time applications. Ice Transactions on Information & Systems 99(7):1877\u20131884. https:\/\/doi.org\/10.1587\/transinf.2015edp7457","journal-title":"Ice Transactions on Information & Systems"},{"key":"3075_CR21","doi-asserted-by":"publisher","unstructured":"Agiomyrgiannakis, Y(2015) Vocaine the vocoder and applications in speech synthesis. In: IEEE International Conference on Acoustics(ICASSP), pp 4230\u20134234 https:\/\/doi.org\/10.1109\/icassp.2015.7178768","DOI":"10.1109\/icassp.2015.7178768"},{"key":"3075_CR22","doi-asserted-by":"publisher","unstructured":"J S, R P, J WR, et al (2018) Natural tts synthesis by conditioning wavenet on mel spectrogram predictions. 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) pp 4779\u20134783 https:\/\/doi.org\/10.1109\/icassp.2018.8461368","DOI":"10.1109\/icassp.2018.8461368"},{"key":"3075_CR23","unstructured":"van den Oord A, Dieleman S, Zen H, Simonyan K, Vinyals O, Graves A, Kalchbrenner N, Senior AW, Kavukcuoglu K (2016) Wavenet A generative model for raw audio. In: The 9th ISCA Speech Synthesis Workshop, Sunnyvale, CA, USA, 13-15 September 2016, ISCA, p 125"},{"key":"3075_CR24","unstructured":"Arik S\u00d6, Chrzanowski M, Coates A, Diamos GF, Gibiansky A, Kang Y, Li X, Miller J, Ng AY, Raiman J, Sengupta S, Shoeybi M (2017) Deep voice Real-time neural text-to-speech. In: Precup D, Teh YW (eds) Proceedings of the 34th International Conference on Machine Learning, ICML 2017, Sydney, NSW, Australia, 6-11 August 2017, PMLR, Proceedings of Machine Learning Research, vol 70, pp 195\u2013204"},{"key":"3075_CR25","doi-asserted-by":"publisher","unstructured":"Wang Y, Skerry-Ryan R, Stanton D, Wu Y, Weiss RJ, Jaitly N, Yang Z, Xiao Y, Chen Z, Bengio S, Le Q, Agiomyrgiannakis Y, Clark R, Saurous RA (2017) Tacotron Towards end-to-end speech synthesis. In: Interspeech 2017, 18th Annual Conference of the International Speech Communication Association, Stockholm, Sweden, August 20-24, 2017, ISCA, pp 4006\u20134010 https:\/\/doi.org\/10.21437\/interspeech.2017-1452","DOI":"10.21437\/interspeech.2017-1452"},{"key":"3075_CR26","unstructured":"Gibiansky A, Arik S\u00d6, Diamos GF, Miller J, Peng K, Ping W, Raiman J, Zhou Y (2017) Deep voice 2 Multi-speaker neural text-to-speech. In: Guyon I, von Luxburg U, Bengio S, Wallach HM, Fergus R, Vishwanathan SVN, Garnett R (eds) Advances in Neural Information Processing Systems 30 Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA, pp 2962\u20132970"},{"key":"3075_CR27","unstructured":"Ping W, Peng K, Gibiansky A, Arik S\u00d6, Kannan A, Narang S, Raiman J, Miller J (2018) Deep voice 3 Scaling text-to-speech with convolutional sequence learning. In: 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings, OpenReview.net"},{"key":"3075_CR28","doi-asserted-by":"publisher","unstructured":"Yasuda Y, Wang X, Takaki S, Yamagishi J (2019) Investigation of enhanced tacotron text-to-speech synthesis systems with self-attention for pitch accent language. In: ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp 6905\u20136909 https:\/\/doi.org\/10.1109\/ICASSP.2019.8682353","DOI":"10.1109\/ICASSP.2019.8682353"},{"key":"3075_CR29","doi-asserted-by":"publisher","unstructured":"Liu R, Sisman B, Li J, Bao F, Gao G, Li H (2020) Teacher-student training for robust tacotron-based TTS. In: 2020 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2020, Barcelona, Spain, May 4-8, 2020, IEEE, pp 6274\u20136278 https:\/\/doi.org\/10.1109\/ICASSP40776.2020.9054681","DOI":"10.1109\/ICASSP40776.2020.9054681"},{"key":"3075_CR30","doi-asserted-by":"publisher","unstructured":"Yang F, Yang S, Zhu P, Yan P, Xie L (2019) Improving mandarin end-to-end speech synthesis by self-attention and learnable gaussian bias. In: 2019 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU), pp 208\u2013213 https:\/\/doi.org\/10.1109\/ASRU46091.2019.9003949","DOI":"10.1109\/ASRU46091.2019.9003949"},{"key":"3075_CR31","doi-asserted-by":"publisher","unstructured":"Lu Y, Dong M, Chen Y (2019) Implementing prosodic phrasing in chinese end-to-end speech synthesis. In: IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2019, Brighton, United Kingdom, May 12-17, 2019, IEEE, pp 7050\u20137054 https:\/\/doi.org\/10.1109\/ICASSP.2019.8682368","DOI":"10.1109\/ICASSP.2019.8682368"},{"key":"3075_CR32","doi-asserted-by":"publisher","unstructured":"Pan J, Yin X, Zhang Z, Liu S, Zhang Y, Ma Z, Wang Y (2020) A unified sequence-to-sequence front-end model for mandarin text-to-speech synthesis. In: 2020 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2020, Barcelona, Spain, May 4-8, 2020, IEEE, pp 6689\u20136693 https:\/\/doi.org\/10.1109\/ICASSP40776.2020.9053390","DOI":"10.1109\/ICASSP40776.2020.9053390"},{"issue":"10","key":"3075_CR33","doi-asserted-by":"publisher","first-page":"1925","DOI":"10.13700\/j.bh.1001-5965.2014.0771","volume":"41","author":"S Jing","year":"2015","unstructured":"Jing S, Mao X, Chen L et al (2015) Annotation and consistency detection of chinese dual-mode emotional speech database. Journal of Beijing University of Aeronautics and Astronautics 41(10):1925\u20131934. https:\/\/doi.org\/10.13700\/j.bh.1001-5965.2014.0771","journal-title":"Journal of Beijing University of Aeronautics and Astronautics"},{"key":"3075_CR34","doi-asserted-by":"publisher","first-page":"012191","DOI":"10.1088\/1742-6596\/1544\/1\/012191","volume":"1544","author":"P Chen","year":"2020","unstructured":"Chen P, Chen L, Mao X (2020) Content classification with electroglottograph. Journal of Physics Conference Series 1544:012191. https:\/\/doi.org\/10.1088\/1742-6596\/1544\/1\/012191","journal-title":"Journal of Physics Conference Series"},{"key":"3075_CR35","doi-asserted-by":"publisher","unstructured":"Irie K, Tuske Z, Alkhouli T, Schluter R, Ney H (2016) Lstm, gru, highway and a bit of attention An empirical overview for language modeling in speech recognition. In: Interspeech 2016 https:\/\/doi.org\/10.21437\/interspeech.2016-491","DOI":"10.21437\/interspeech.2016-491"},{"issue":"5","key":"3075_CR36","doi-asserted-by":"publisher","first-page":"681","DOI":"10.1016\/j.aeue.2016.02.006","volume":"70","author":"N Prukkanon","year":"2016","unstructured":"Prukkanon N, Chamnongthai K, Miyanaga Y (2016) F0 contour approximation model for a one-stream tonal word recognition system. AEUE - International Journal of Electronics and Communications 70(5):681\u2013688. https:\/\/doi.org\/10.1016\/j.aeue.2016.02.006","journal-title":"AEUE - International Journal of Electronics and Communications"},{"key":"3075_CR37","doi-asserted-by":"publisher","first-page":"45","DOI":"10.3969\/j.issn.1003-0077.2001.02.007","volume":"15","author":"Z Xiao","year":"2001","unstructured":"Xiao Z (2001) An approach of fundamental frequencies smoothing for chinese tone recognition. Journal of Chinese Information Processing 15:45\u201350. https:\/\/doi.org\/10.3969\/j.issn.1003-0077.2001.02.007","journal-title":"Journal of Chinese Information Processing"},{"key":"3075_CR38","doi-asserted-by":"publisher","unstructured":"Chiu JPC, Nichols E (2015) Named entity recognition with bidirectional lstm-cnns. Computer Science. https:\/\/doi.org\/10.1162\/tacl_a_00104","DOI":"10.1162\/tacl_a_00104"},{"key":"3075_CR39","doi-asserted-by":"publisher","unstructured":"Shen J, Pang R, Weiss R, Schuster M, Jaitly N, Yang Z, Chen Z, Zhang Y, Wang Y, Skerry-Ryan R, Saurous R, Agiomyrgiannakis Y, Wu Y (2018) Natural tts synthesis by conditioning wavenet on mel spectrogram predictions. 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) pp 4779\u20134783 https:\/\/doi.org\/10.1109\/icassp.2018.8461368","DOI":"10.1109\/icassp.2018.8461368"},{"key":"3075_CR40","doi-asserted-by":"publisher","unstructured":"Chollet F (2017) Xception Deep learning with depthwise separable convolutions. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) https:\/\/doi.org\/10.1109\/cvpr.2017.195","DOI":"10.1109\/cvpr.2017.195"},{"key":"3075_CR41","unstructured":"Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H (2017) Mobilenets Efficient convolutional neural networks for mobile vision applications. CoRR abs\/1704.04861, 1704.04861"},{"issue":"4","key":"3075_CR42","doi-asserted-by":"publisher","first-page":"1045","DOI":"10.1007\/s10489-019-01587-1","volume":"50","author":"J Wang","year":"2020","unstructured":"Wang J, Xiong H, Wang H, Nian X (2020) Adscnet asymmetric depthwise separable convolution for semantic segmentation in real-time. Appl Intell 50(4):1045\u20131056. https:\/\/doi.org\/10.1007\/s10489-019-01587-1","journal-title":"Appl Intell"},{"key":"3075_CR43","doi-asserted-by":"publisher","unstructured":"Wang Z, Yan W, Oates T (2017) Time series classification from scratch with deep neural networks A strong baseline. 2017 International Joint Conference on Neural Networks (IJCNN) https:\/\/doi.org\/10.1109\/ijcnn.2017.7966039","DOI":"10.1109\/ijcnn.2017.7966039"},{"key":"3075_CR44","doi-asserted-by":"publisher","unstructured":"Jing L, Gulcehre C, Peurifoy J, Shen Y, Tegmark M, Solja\u010di\u0107 Bengio Y (2017) Gated orthogonal recurrent units On learning to forget. Neural Computation 31:765\u2013783. https:\/\/doi.org\/10.1162\/neco_a_01174","DOI":"10.1162\/neco_a_01174"},{"key":"3075_CR45","unstructured":"Kingma DP, Ba J (2015) Adam A method for stochastic optimization. In: Bengio Y, LeCun Y (eds) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings"},{"key":"3075_CR46","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-021-02446-8","author":"X Hu","year":"2021","unstructured":"Hu X, Jing L, Sehar U (2021) Joint pyramid attention network for real-time semantic segmentation of urban scenes. Applied Intelligence. https:\/\/doi.org\/10.1007\/s10489-021-02446-8","journal-title":"Applied Intelligence"},{"key":"3075_CR47","doi-asserted-by":"crossref","unstructured":"Kubichek R (1993) Mel-cepstral distance measure for objective speech quality assessment. Proceedings of IEEE Pacific Rim Conference on Communications Computers and Signal Processing 1:125\u2013128 vol.1","DOI":"10.1109\/PACRIM.1993.407206"},{"key":"3075_CR48","doi-asserted-by":"publisher","unstructured":"Yang S, Gao T, Wang J, Deng B, Linares-Barranco B (2021) Efficient Spike-Driven Learning With Dendritic Event-Based Processing. Frontiers in Neuroscience 15. https:\/\/doi.org\/10.3389\/fnins.2021.601109","DOI":"10.3389\/fnins.2021.601109"},{"key":"3075_CR49","doi-asserted-by":"publisher","unstructured":"Yang S, Wang J, Deng B, Azghadi MR, Linares-Barranco B (2021b) Neuromorphic Context-Dependent Learning Framework With Fault-Tolerant Spike Routing. IEEE Transactions on Neural Networks and Learning Systems pp 1\u201315 https:\/\/doi.org\/10.1109\/TNNLS.2021.3084250","DOI":"10.1109\/TNNLS.2021.3084250"},{"issue":"04","key":"3075_CR50","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1142\/S0129065709002002","volume":"19","author":"S Ghosh-Dastidar","year":"2009","unstructured":"Ghosh-Dastidar S, Adeli H (2009) Spiking neural networks. International Journal of Neural Systems 19(04):295\u2013308. https:\/\/doi.org\/10.1142\/S0129065709002002","journal-title":"International Journal of Neural Systems"},{"key":"3075_CR51","doi-asserted-by":"publisher","first-page":"88","DOI":"10.3389\/fnins.2020.00088","volume":"14","author":"SA Lobov","year":"2020","unstructured":"Lobov SA, Mikhaylov AN, Kazantsev VB (2020) Spatial Properties of STDP in a Self-Learning Spiking Neural Network Enable Controlling a Mobile Robot. Frontiers in Neuroscience 14:88. https:\/\/doi.org\/10.3389\/fnins.2020.00088","journal-title":"Frontiers in Neuroscience"},{"issue":"7","key":"3075_CR52","doi-asserted-by":"publisher","first-page":"2490","DOI":"10.1109\/TCYB.2018.2823730","volume":"49","author":"S Yang","year":"2019","unstructured":"Yang S, Wang J, Deng B, Liu C, Li H, Fietkiewicz C, Loparo KA (2019) Real-Time Neuromorphic System for Large-Scale Conductance-Based Spiking Neural Networks. IEEE Transactions on Cybernetics 49(7):2490\u20132503. https:\/\/doi.org\/10.1109\/TCYB.2018.2823730","journal-title":"IEEE Transactions on Cybernetics"},{"key":"3075_CR53","doi-asserted-by":"publisher","unstructured":"Yang S, Wang J, Hao X, Li H, Wei X, deng B, Loparo K (2021a) Bicoss Toward large-scale cognition brain with multigranular neuromorphic architecture. IEEE Transactions on Neural Networks and Learning Systems PP:1\u201315 https:\/\/doi.org\/10.1109\/TNNLS.2020.3045492","DOI":"10.1109\/TNNLS.2020.3045492"},{"key":"3075_CR54","doi-asserted-by":"publisher","unstructured":"Yang S, Wang J, Zhang N, Deng B, Pang Y, Azghadi MR (2021b) CerebelluMorphic Large-Scale Neuromorphic Model and Architecture for Supervised Motor Learning. IEEE Transactions on Neural Networks and Learning Systems pp 1\u201315 https:\/\/doi.org\/10.1109\/TNNLS.2021.3057070","DOI":"10.1109\/TNNLS.2021.3057070"},{"issue":"1","key":"3075_CR55","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1109\/TNNLS.2019.2899936","volume":"31","author":"S Yang","year":"2020","unstructured":"Yang S, Deng B, Wang J, Li H, Lu M, Che Y, Wei X, Loparo KA (2020) Scalable Digital Neuromorphic Architecture for Large-Scale Biophysically Meaningful Neural Network With Multi-Compartment Neurons. IEEE Transactions on Neural Networks and Learning Systems 31(1):148\u2013162. https:\/\/doi.org\/10.1109\/TNNLS.2019.2899936","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-021-03075-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-021-03075-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-021-03075-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,20]],"date-time":"2024-09-20T04:44:48Z","timestamp":1726807488000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-021-03075-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,12]]},"references-count":55,"journal-issue":{"issue":"13","published-print":{"date-parts":[[2022,10]]}},"alternative-id":["3075"],"URL":"https:\/\/doi.org\/10.1007\/s10489-021-03075-x","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,12]]},"assertion":[{"value":"2 December 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 March 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}