{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T03:04:25Z","timestamp":1771902265584,"version":"3.50.1"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2022,2,19]],"date-time":"2022-02-19T00:00:00Z","timestamp":1645228800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,2,19]],"date-time":"2022-02-19T00:00:00Z","timestamp":1645228800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100010903","name":"Key Programme","doi-asserted-by":"publisher","award":["U1836220"],"award-info":[{"award-number":["U1836220"]}],"id":[{"id":"10.13039\/501100010903","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010242","name":"Jiangsu Planned Projects for Postdoctoral Research Funds","doi-asserted-by":"publisher","award":["2019K222"],"award-info":[{"award-number":["2019K222"]}],"id":[{"id":"10.13039\/501100010242","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013088","name":"Qinglan Project of Jiangsu Province of Chin","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100013088","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Jiangsu Province key research and development plan","award":["BE2020036"],"award-info":[{"award-number":["BE2020036"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2022,6]]},"DOI":"10.1007\/s00521-022-06968-1","type":"journal-article","created":{"date-parts":[[2022,2,19]],"date-time":"2022-02-19T19:02:36Z","timestamp":1645297356000},"page":"9831-9845","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["A context aware-based deep neural network approach for simultaneous speech denoising and dereverberation"],"prefix":"10.1007","volume":"34","author":[{"given":"Sidheswar","family":"Routray","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0616-4431","authenticated-orcid":false,"given":"Qirong","family":"Mao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,2,19]]},"reference":[{"issue":"3","key":"6968_CR1","doi-asserted-by":"publisher","first-page":"572","DOI":"10.1109\/TASLP.2016.2641904","volume":"25","author":"CSJ Doire","year":"2017","unstructured":"Doire CSJ, Brookes M, Naylor PA, Hicks CM, Betts D, Dmour MA, Holdt-Jensen S (2017) Single-channel online enhancement of speech corrupted by reverberation and noise. IEEE\/ACM Trans Audio Speech Lang Process 25(3):572\u2013587","journal-title":"IEEE\/ACM Trans Audio Speech Lang Process"},{"issue":"7","key":"6968_CR2","doi-asserted-by":"publisher","first-page":"1492","DOI":"10.1109\/TASLP.2017.2696307","volume":"25","author":"DS Williamson","year":"2017","unstructured":"Williamson DS, Wang D (2017) Time-frequency masking in the complex domain for speech dereverberation and denoising. IEEE\/ACM Trans Audio Speech Lang Process 25(7):1492\u20131501","journal-title":"IEEE\/ACM Trans Audio Speech Lang Process"},{"key":"6968_CR3","doi-asserted-by":"publisher","unstructured":"Nakatani T, Ikeshita R., Kinoshita K, Sawada H, Araki S (2021) Blind and neural network-guided convolutional beamformer for joint denoising, dereverberation, and source separation. In: ICASSP 2021 - 2021 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 6129\u20136133, https:\/\/doi.org\/10.1109\/ICASSP39728.2021.9414264","DOI":"10.1109\/ICASSP39728.2021.9414264"},{"key":"6968_CR4","doi-asserted-by":"publisher","first-page":"2267","DOI":"10.1109\/TASLP.2020.3013118","volume":"28","author":"T Nakatani","year":"2020","unstructured":"Nakatani T, Boeddeker C, Kinoshita K, Ikeshita R, Delcroix M, Haeb-Umbach R (2020) Jointly optimal denoising, dereverberation, and source separation. IEEE\/ACM Trans Audio Speech Lang Process 28:2267\u20132282. https:\/\/doi.org\/10.1109\/TASLP.2020.3013118","journal-title":"IEEE\/ACM Trans Audio Speech Lang Process"},{"key":"6968_CR5","doi-asserted-by":"publisher","unstructured":"Baby D, Bourlard H (2021) Speech dereverberation using variational autoencoders. In: ICASSP 2021\u20132021 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 5784\u20135788, https:\/\/doi.org\/10.1109\/ICASSP39728.2021.9414736","DOI":"10.1109\/ICASSP39728.2021.9414736"},{"issue":"3","key":"6968_CR6","doi-asserted-by":"publisher","first-page":"774","DOI":"10.1109\/TSA.2005.858066","volume":"14","author":"M Wu","year":"2006","unstructured":"Wu M, Wang D (2006) A two-stage algorithm for one-microphone reverberant speech enhancement. IEEE Trans Audio Speech Lang Process 14(3):774\u2013784","journal-title":"IEEE Trans Audio Speech Lang Process"},{"key":"6968_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.specom.2019.03.002","volume":"109","author":"M Parchami","year":"2019","unstructured":"Parchami M, Amindavar H, Zhu W (2019) Speech reverberation suppression for time-varying environments using weighted prediction error method with time-varying autoregressive model. Speech Commun 109:1\u201314. https:\/\/doi.org\/10.1016\/j.specom.2019.03.002","journal-title":"Speech Commun"},{"key":"6968_CR8","unstructured":"Delcroix M, Yoshioka T, Ogawa A, Kubo Y, Fujimoto M, Ito N, Kinoshita K, Espi M, Hori T, Nakatani T, Nakamura A (2014) Linear prediction-based dereverberation with advanced speech enhancement and recognition technologies for the reverb challenge, In: Proceedings of the REVERB challenge workshop, vol 1, pp 1\u20138"},{"issue":"2","key":"6968_CR9","doi-asserted-by":"publisher","first-page":"394","DOI":"10.1109\/TASLP.2014.2372342","volume":"23","author":"B Schwartz","year":"2015","unstructured":"Schwartz B, Gannot S, Habets EAP (2015) Online speech dereverberation using Kalman filter and EM algorithm. IEEE\/ACM Trans Audio Speech Lang Process 23(2):394\u2013406","journal-title":"IEEE\/ACM Trans Audio Speech Lang Process"},{"key":"6968_CR10","doi-asserted-by":"crossref","unstructured":"Cohen A, Stemmer G, Ingalsuo S, Markovich-Golan S (2017) Combined weighted prediction error and minimum variance distortionless response for dereverberation. In: IEEE international conference on acoustics, speech and signal processing, pp 446\u2013450","DOI":"10.1109\/ICASSP.2017.7952195"},{"issue":"4","key":"6968_CR11","doi-asserted-by":"publisher","first-page":"888","DOI":"10.1016\/j.csl.2014.01.001","volume":"28","author":"F Weninger","year":"2014","unstructured":"Weninger F, Geiger J, Wollmer M, Schuller B, Rigoll G (2014) Feature enhancement by deep LSTM networks for ASR in reverberant multisource environments. Comput Speech Lang 28(4):888\u2013902","journal-title":"Comput Speech Lang"},{"issue":"6","key":"6968_CR12","doi-asserted-by":"publisher","first-page":"982","DOI":"10.1109\/TASLP.2015.2416653","volume":"23","author":"K Han","year":"2015","unstructured":"Han K, Wang Y, Wang D, Woods WS, Merks I, Zhang T (2015) Learning spectral mapping for speech dereverberation and denoising. IEEE\/ACM Trans Audio Speech Lang Process 23(6):982\u2013992","journal-title":"IEEE\/ACM Trans Audio Speech Lang Process"},{"issue":"1","key":"6968_CR13","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1186\/s13634-015-0300-4","volume":"2016","author":"X Xiao","year":"2016","unstructured":"Xiao X, Zhao S, Nguyen DHH, Zhong X, Jones DL, Chng ES, Li H (2016) Speech dereverberation for enhancement and recognition using dynamic features constrained deep neural networks and feature adaptation. EURASIP J Adv Signal Process 2016(1):4","journal-title":"EURASIP J Adv Signal Process"},{"issue":"1","key":"6968_CR14","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1109\/TASLP.2016.2623559","volume":"25","author":"B Wu","year":"2017","unstructured":"Wu B, Li K, Yang M, Lee C-H (2017) A reverberation-time aware approach to speech dereverberation based on deep neural networks. IEEE\/ACM Trans Audio Speech Lang Process 25(1):102\u2013111","journal-title":"IEEE\/ACM Trans Audio Speech Lang Process"},{"key":"6968_CR15","doi-asserted-by":"crossref","unstructured":"Zhao Y, Wang Z-Q, Wang DL (2017) A two-stage algorithm for noisy and reverberant speech enhancement. In: Proceedings of ICASSP, pp 5580\u20135584","DOI":"10.1109\/ICASSP.2017.7953224"},{"key":"6968_CR16","doi-asserted-by":"crossref","unstructured":"Raikar A, Basu S, Hegde RM (2018) Single channel joint speech dereverberation and denoising using deep priors. In: 2018 IEEE global conference on signal and information processing (GlobalSIP). IEEE, pp 216\u2013220","DOI":"10.1109\/GlobalSIP.2018.8646327"},{"key":"6968_CR17","doi-asserted-by":"publisher","first-page":"941","DOI":"10.1109\/TASLP.2020.2975902","volume":"28","author":"Z-Q Wang","year":"2020","unstructured":"Wang Z-Q, Wang D (2020) Deep learning based target cancellation for speech dereverberation. IEEE\/ACM Trans Audio Speech Lang Process 28:941\u2013950. https:\/\/doi.org\/10.1109\/TASLP.2020.2975902","journal-title":"IEEE\/ACM Trans Audio Speech Lang Process"},{"issue":"4","key":"6968_CR18","doi-asserted-by":"publisher","first-page":"744","DOI":"10.1109\/TCDS.2019.2953620","volume":"12","author":"T Hussain","year":"2020","unstructured":"Hussain T, Siniscalchi SM, Wang H-LS, Tsao Y, Salerno VM, Liao W-H (2020) Ensemble hierarchical extreme learning machine for speech dereverberation. IEEE Trans Cognit Dev Syst 12(4):744\u2013758. https:\/\/doi.org\/10.1109\/TCDS.2019.2953620","journal-title":"IEEE Trans Cognit Dev Syst"},{"key":"6968_CR19","doi-asserted-by":"publisher","first-page":"238","DOI":"10.1016\/j.neunet.2021.04.023","volume":"141","author":"H Chen","year":"2021","unstructured":"Chen H, Zhang P (2021) A dual-stream deep attractor network with multi-domain learning for speech dereverberation and separation. Neural Netw 141:238\u2013248. https:\/\/doi.org\/10.1016\/j.neunet.2021.04.023","journal-title":"Neural Netw"},{"issue":"16","key":"6968_CR20","doi-asserted-by":"publisher","first-page":"9993","DOI":"10.1007\/s00521-021-05767-4","volume":"33","author":"RQ Albuquerque","year":"2021","unstructured":"Albuquerque RQ, Mello CAB (2021) Automatic no-reference speech quality assessment with convolutional neural networks. Neural Comput Appl 33(16):9993\u201310003","journal-title":"Neural Comput Appl"},{"key":"6968_CR21","doi-asserted-by":"publisher","first-page":"101270","DOI":"10.1016\/j.csl.2021.101270","volume":"71","author":"S Routray","year":"2022","unstructured":"Routray S, Mao Q (2022) Phase sensitive masking-based single channel speech enhancement using conditional generative adversarial network. Comput Speech Lang 71:101270. https:\/\/doi.org\/10.1016\/j.csl.2021.101270","journal-title":"Comput Speech Lang"},{"key":"6968_CR22","doi-asserted-by":"crossref","unstructured":"Kanda N et al. (2019) Guided source separation meets a strong asr backend: Hitachi\/Paderborn university joint investigation for dinner party ASR. In: Proceedings of the Interspeech, pp 1248\u20131252","DOI":"10.21437\/Interspeech.2019-1167"},{"issue":"6","key":"6968_CR23","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1109\/MSP.2019.2918706","volume":"36","author":"R Haeb-Umbach","year":"2019","unstructured":"Haeb-Umbach R et al (2019) Speech processing for digital home assistants. IEEE Signal Process Mag 36(6):111\u2013124","journal-title":"IEEE Signal Process Mag"},{"key":"6968_CR24","doi-asserted-by":"crossref","unstructured":"Togami M (2015) Multichannel online speech dereverberation under noisy environments. In: Proceedings of the 23rd European conference on signal processing, pp 1078\u20131082","DOI":"10.1109\/EUSIPCO.2015.7362549"},{"issue":"6","key":"6968_CR25","doi-asserted-by":"publisher","first-page":"1119","DOI":"10.1109\/TASLP.2018.2811247","volume":"26","author":"S Braun","year":"2018","unstructured":"Braun S, Habets EAP (2018) Linear prediction based online dereverberation and noise reduction using alternating Kalman filters. IEEE\/ACM Trans Audio Speech Lang Process 26(6):1119\u20131129","journal-title":"IEEE\/ACM Trans Audio Speech Lang Process"},{"key":"6968_CR26","doi-asserted-by":"crossref","unstructured":"Dietzen T, Doclo S, Moonen M, van Waterschoot T (2018) Joint multi-microphone speech dereverberation and noise reduction using integrated sidelobe cancellation and linear prediction. In: Proceedings of the 6th international workshop on acoustic signal enhancement, pp 221\u2013225","DOI":"10.1109\/IWAENC.2018.8521250"},{"key":"6968_CR27","doi-asserted-by":"crossref","unstructured":"Mohammadiha N, Smaragdis P, Doclo S (2015) Joint acoustic and spectral modeling for speech dereverberation using non-negative representations. In: 2015 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 4410\u20134414. IEEE","DOI":"10.1109\/ICASSP.2015.7178804"},{"issue":"12","key":"6968_CR28","doi-asserted-by":"publisher","first-page":"1849","DOI":"10.1109\/TASLP.2014.2352935","volume":"22","author":"Y Wang","year":"2014","unstructured":"Wang Y, Narayanan A, Wang DL (2014) On training targets for supervised speech separation. IEEE\/ACM Trans Audio Speech Lang Process 22(12):1849\u20131858","journal-title":"IEEE\/ACM Trans Audio Speech Lang Process"},{"issue":"10","key":"6968_CR29","doi-asserted-by":"publisher","first-page":"1702","DOI":"10.1109\/TASLP.2018.2842159","volume":"26","author":"D Wang","year":"2018","unstructured":"Wang D, Chen J (2018) Supervised speech separation based on deep learning: an overview. IEEE\/ACM Trans Audio Speech Lang Process 26(10):1702\u20131726","journal-title":"IEEE\/ACM Trans Audio Speech Lang Process"},{"key":"6968_CR30","unstructured":"Shao Y, Srinivasan S, Wang DL (2008) Robust speaker identification using auditory features and computational auditory scene analysis. In: Proceedings of ICASSP, pp 1589\u20131592"},{"key":"6968_CR31","doi-asserted-by":"publisher","first-page":"578","DOI":"10.1109\/89.326616","volume":"2","author":"H Hermansky","year":"1994","unstructured":"Hermansky H, Morgan N (1994) RASTA processing of speech. IEEE Trans Speech Audio Proc 2:578\u2013589","journal-title":"IEEE Trans Speech Audio Proc"},{"key":"6968_CR32","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1109\/TAU.1969.1162058","volume":"17","author":"EH Rothauser","year":"1969","unstructured":"Rothauser EH et al (1969) IEEE recommended practice for speech quality measurements. IEEE Trans Audio Electroacoust 17:225\u2013246","journal-title":"IEEE Trans Audio Electroacoust"},{"key":"6968_CR33","unstructured":"Habets E (2010) Room impulse response generator (http:\/\/home.tiscali.nl\/ehabets\/rir generator.html)"},{"key":"6968_CR34","doi-asserted-by":"publisher","first-page":"943","DOI":"10.1121\/1.382599","volume":"65","author":"JB Allen","year":"1979","unstructured":"Allen JB, Berkley DA (1979) Image method for efficiently simulating small room acoustics. J Acoust Soc Am 65:943\u2013950","journal-title":"J Acoust Soc Am"},{"issue":"3","key":"6968_CR35","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1016\/0167-6393(93)90095-3","volume":"12","author":"A Varga","year":"1993","unstructured":"Varga A, Steeneken HJ (1993) Assessment for automatic speech recognition: Ii. noisex-92: A database and an experiment to study the effect of additive noise on speech recognition systems. Speech Commun 12(3):247\u2013251","journal-title":"Speech Commun"},{"key":"6968_CR36","first-page":"1","volume":"7","author":"K Kinoshita","year":"2016","unstructured":"Kinoshita K, Delcroix M, Gannot S, Habets E, Haeb-Umbach R, Kellermann W, Leutnant V, Maas R, Nakatani T, Raj B, Sehr A, Yoshioka T (2016) A summary of the reverb challenge: state-of-the-art and remaining challenges in reverberant speech processing research. EURASIP J Adv Signal Process 7:1\u201319","journal-title":"EURASIP J Adv Signal Process"},{"key":"6968_CR37","doi-asserted-by":"crossref","unstructured":"Robinson T, Fransen J, Pye D, Foote J, Renals S (1995) WSJCAMO: a british english speech corpus for large vocabulary continuous speech recognition. In: International conference on acoustics, speech, and signal processing (ICASSP), pp 81\u201384","DOI":"10.1109\/ICASSP.1995.479278"},{"key":"6968_CR38","doi-asserted-by":"crossref","unstructured":"Lincoln M, McCowan I, Vepa J, Maganti HK (2005) The multichannel wall street journal audio visual corpus (MC-WSJ-AV): specification and initial experiments. In: IEEE workshop on automatic speech recognition and understanding, pp 357\u2013362","DOI":"10.1109\/ASRU.2005.1566470"},{"key":"6968_CR39","doi-asserted-by":"crossref","unstructured":"Garofolo JS, Lamel LF, Fisher WM, Fiscus JG, Pallett DS (1993) Darpa timit acoustic-phonetic continous speech corpus cd-rom. nist speech disc 1-1.1, NASA STI\/Recon technical report n, vol 93","DOI":"10.6028\/NIST.IR.4930"},{"key":"6968_CR40","unstructured":"Hu G (2019) 100 nonspeech sounds 2006 [oneline], Technical Report. Available online: http:\/\/web.cse.ohiostate.edu\/pnl\/corpus\/HuNonspeech\/HuCorpus.html (accessed on 22 February 2019), Tech. Rep"},{"key":"6968_CR41","doi-asserted-by":"crossref","unstructured":"Rix A W, Beerends JG, Hollier MP, Hekstra AP (2001) Perceptual evaluation of speech quality (PESQ)-a new method for speech quality assessment of telephone networks and codecs. In: Proceedings of the IEEE international conference on acoustics, speech, and signal processing, vol 2, pp 749\u2013752","DOI":"10.1109\/ICASSP.2001.941023"},{"issue":"7","key":"6968_CR42","doi-asserted-by":"publisher","first-page":"2125","DOI":"10.1109\/TASL.2011.2114881","volume":"19","author":"CH Taal","year":"2011","unstructured":"Taal CH, Hendriks RC, Heusdens R, Jensen J (2011) An algorithm for intelligibility prediction of time-frequency weighted noisy speech. IEEE Trans Audio Speech Lang Process 19(7):2125\u20132136","journal-title":"IEEE Trans Audio Speech Lang Process"},{"issue":"1","key":"6968_CR43","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1109\/TASL.2007.911054","volume":"16","author":"Y Hu","year":"2008","unstructured":"Hu Y, Loizou PC (2008) Evaluation of objective quality measures for speech enhancement. IEEE Trans Audio Speech Lang Process 16(1):229\u2013238","journal-title":"IEEE Trans Audio Speech Lang Process"},{"issue":"7","key":"6968_CR44","doi-asserted-by":"publisher","first-page":"1717","DOI":"10.1109\/TASL.2010.2052251","volume":"18","author":"Tomohiro Nakatani","year":"2010","unstructured":"Nakatani Tomohiro, Yoshioka Takuya, Kinoshita Keisuke, Miyoshi Masato, Juang Biing-Hwang (2010) Speech dereverberation based on variance-normalized delayed linear prediction. IEEE Trans Audio Speech Lang Process 18(7):1717\u20131731","journal-title":"IEEE Trans Audio Speech Lang Process"},{"key":"6968_CR45","doi-asserted-by":"publisher","first-page":"1314","DOI":"10.21437\/Interspeech.2018-1296","volume":"2018","author":"Wolfgang Mack","year":"2018","unstructured":"Mack Wolfgang, Chakrabarty Soumitro, Stoter Fabian-Robert, Braun Sebastian, Edler Bernd, Habets Emanuel (2018) Single-channel dereverberation using direct mmse optimization and bidirectional lstm networks. Proc Interspeech 2018:1314\u20131318","journal-title":"Proc Interspeech"},{"key":"6968_CR46","doi-asserted-by":"crossref","unstructured":"Rethage D, Pons J, Serra X (2018) A wavenet for speech denoising. In: 2018 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, pp 5069\u20135073","DOI":"10.1109\/ICASSP.2018.8462417"},{"issue":"6","key":"6968_CR47","doi-asserted-by":"publisher","first-page":"982","DOI":"10.1109\/TASLP.2015.2416653","volume":"23","author":"K Han","year":"2015","unstructured":"Han K, Wang Y, Wang DL, Woods WS, Merks I, Zhang T (2015) Learning spectral mapping for speech dereverberation and denoising. IEEE\/ACM Trans Audio Speech Lang Process 23(6):982\u2013992","journal-title":"IEEE\/ACM Trans Audio Speech Lang Process"},{"key":"6968_CR48","doi-asserted-by":"crossref","unstructured":"Fan C, Tao J, Liu B, Yi J, Wen Z (2020) Joint Training for simultaneous speech denoising and dereverberation with deep embedding representations, INTERSPEECH","DOI":"10.21437\/Interspeech.2020-1225"},{"key":"6968_CR49","doi-asserted-by":"crossref","unstructured":"Nakatani T et al (2020) DNN-supported mask-based convolutional beamforming for simultaneous denoising, dereverberation, and source separation. In: ICASSP 2020\u20132020 ieee international conference on acoustics, speech and signal processing (ICASSP), Barcelona, Spain, pp 6399\u20136403","DOI":"10.1109\/ICASSP40776.2020.9053343"},{"key":"6968_CR50","doi-asserted-by":"crossref","unstructured":"Jeub M, Schafer M, Vary P (2009) A binaural room impulse response database for the evaluation of dereverberation algorithms. In: Proceedings of the international conference on digital signal processing, pp 1\u20135","DOI":"10.1109\/ICDSP.2009.5201259"},{"key":"6968_CR51","doi-asserted-by":"publisher","first-page":"1598","DOI":"10.1109\/TASLP.2020.2995273","volume":"28","author":"Y Zhao","year":"2020","unstructured":"Zhao Y, Wang D, Xu B, Zhang T (2020) Monaural speech dereverberation using temporal convolutional networks with self attention. IEEE\/ACM Trans Audio Speech Lang Process 28:1598\u20131607","journal-title":"IEEE\/ACM Trans Audio Speech Lang Process"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-06968-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-022-06968-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-06968-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,16]],"date-time":"2022-05-16T06:14:23Z","timestamp":1652681663000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-022-06968-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,19]]},"references-count":51,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2022,6]]}},"alternative-id":["6968"],"URL":"https:\/\/doi.org\/10.1007\/s00521-022-06968-1","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,19]]},"assertion":[{"value":"10 April 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 January 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 February 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}