{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T17:01:03Z","timestamp":1773248463110,"version":"3.50.1"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783031083327","type":"print"},{"value":"9783031083334","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-08333-4_18","type":"book-chapter","created":{"date-parts":[[2022,6,16]],"date-time":"2022-06-16T15:52:13Z","timestamp":1655394733000},"page":"216-228","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Speech Emotion Recognition from\u00a0Earnings Conference Calls in\u00a0Predicting Corporate Financial Distress"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5579-1215","authenticated-orcid":false,"given":"Petr","family":"Hajek","sequence":"first","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,6,10]]},"reference":[{"key":"18_CR1","doi-asserted-by":"publisher","first-page":"164","DOI":"10.1016\/j.eswa.2017.10.040","volume":"94","author":"HA Alaka","year":"2018","unstructured":"Alaka, H.A., et al.: Systematic review of bankruptcy prediction models: towards a framework for tool selection. Expert Syst. Appl. 94, 164\u2013184 (2018)","journal-title":"Expert Syst. Appl."},{"key":"18_CR2","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-3-642-41016-1_1","volume-title":"Engineering Applications of Neural Networks","author":"P H\u00e1jek","year":"2013","unstructured":"H\u00e1jek, P., Olej, V.: Evaluating sentiment in annual reports for financial distress prediction using neural networks and support vector machines. In: Iliadis, L., Papadopoulos, H., Jayne, C. (eds.) EANN 2013. CCIS, vol. 384, pp. 1\u201310. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-41016-1_1"},{"issue":"4","key":"18_CR3","doi-asserted-by":"publisher","first-page":"721","DOI":"10.3846\/20294913.2014.979456","volume":"20","author":"P Hajek","year":"2014","unstructured":"Hajek, P., Olej, V., Myskova, R.: Forecasting corporate financial performance using sentiment in annual reports for stakeholders\u2019 decision-making. Technol. Econ. Dev. Econ. 20(4), 721\u2013738 (2014)","journal-title":"Technol. Econ. Dev. Econ."},{"issue":"2","key":"18_CR4","doi-asserted-by":"publisher","first-page":"743","DOI":"10.1016\/j.ejor.2018.10.024","volume":"274","author":"F Mai","year":"2019","unstructured":"Mai, F., Tian, S., Lee, C., Ma, L.: Deep learning models for bankruptcy prediction using textual disclosures. Eur. J. Oper. Res. 274(2), 743\u2013758 (2019)","journal-title":"Eur. J. Oper. Res."},{"key":"18_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/01605682.2020.1784049","volume":"73","author":"BH Nguyen","year":"2022","unstructured":"Nguyen, B.H., Huynh, V.N.: Textual analysis and corporate bankruptcy: a financial dictionary-based sentiment approach. J. Oper. Res. Soc. 73, 1\u201320 (2022)","journal-title":"J. Oper. Res. Soc."},{"issue":"4","key":"18_CR6","doi-asserted-by":"publisher","first-page":"992","DOI":"10.1016\/j.jbankfin.2011.10.013","volume":"36","author":"SM Price","year":"2012","unstructured":"Price, S.M., Doran, J.S., Peterson, D.R., Bliss, B.A.: Earnings conference calls and stock returns: the incremental informativeness of textual tone. J. Bank. Financ. 36(4), 992\u20131011 (2012)","journal-title":"J. Bank. Financ."},{"issue":"6","key":"18_CR7","doi-asserted-by":"publisher","first-page":"1422","DOI":"10.3846\/tede.2020.13758","volume":"26","author":"R Myskova","year":"2020","unstructured":"Myskova, R., Hajek, P.: Mining risk-related sentiment in corporate annual reports and its effect on financial performance. Technol. Econ. Dev. Econ. 26(6), 1422\u20131443 (2020)","journal-title":"Technol. Econ. Dev. Econ."},{"issue":"1","key":"18_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1111\/j.1540-6261.2011.01705.x","volume":"67","author":"WJ Mayew","year":"2012","unstructured":"Mayew, W.J., Venkatachalam, M.: The power of voice: managerial affective states and future firm performance. J. Financ. 67(1), 1\u201343 (2012)","journal-title":"J. Financ."},{"issue":"4","key":"18_CR9","doi-asserted-by":"publisher","first-page":"515","DOI":"10.1007\/s11146-016-9557-0","volume":"54","author":"SM Price","year":"2017","unstructured":"Price, S.M., Seiler, M.J., Shen, J.: Do investors infer vocal cues from CEOs during quarterly REIT conference calls? J. Real Estate Financ. Econ. 54(4), 515\u2013557 (2017)","journal-title":"J. Real Estate Financ. Econ."},{"issue":"2","key":"18_CR10","doi-asserted-by":"publisher","first-page":"349","DOI":"10.1111\/j.1475-679X.2011.00433.x","volume":"50","author":"JL Hobson","year":"2012","unstructured":"Hobson, J.L., Mayew, W.J., Venkatachalam, M.: Analyzing speech to detect financial misreporting. J. Account. Res. 50(2), 349\u2013392 (2012)","journal-title":"J. Account. Res."},{"key":"18_CR11","doi-asserted-by":"crossref","unstructured":"Yang, L., Ng, T.L.J., Smyth, B., Dong, R.: HTML: hierarchical transformer-based multi-task learning for volatility prediction. In: Proceedings of the Web Conference 2020, pp. 441\u2013451 (2020)","DOI":"10.1145\/3366423.3380128"},{"key":"18_CR12","doi-asserted-by":"crossref","unstructured":"Sawhney, R., Khanna, P., Aggarwal, A., Jain, T., Mathur, P., Shah, R.: VolTAGE: volatility forecasting via text-audio fusion with graph convolution networks for earnings calls. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 8001\u20138013 (2020)","DOI":"10.18653\/v1\/2020.emnlp-main.643"},{"key":"18_CR13","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1016\/j.dss.2015.04.006","volume":"74","author":"CS Throckmorton","year":"2015","unstructured":"Throckmorton, C.S., Mayew, W.J., Venkatachalam, M., Collins, L.M.: Financial fraud detection using vocal, linguistic and financial cues. Decis. Support Syst. 74, 78\u201387 (2015)","journal-title":"Decis. Support Syst."},{"issue":"2","key":"18_CR14","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1177\/0261927X15586792","volume":"35","author":"J Burgoon","year":"2016","unstructured":"Burgoon, J., et al.: Which spoken language markers identify deception in high-stakes settings? Evidence from earnings conference calls. J. Lang. Soc. Psychol. 35(2), 123\u2013157 (2016)","journal-title":"J. Lang. Soc. Psychol."},{"key":"18_CR15","doi-asserted-by":"crossref","unstructured":"Sawhney, R., Mathur, P., Mangal, A., Khanna, P., Shah, R.R., Zimmermann, R.: Multi-modal multi-task financial risk forecasting. In: Proceedings of the 28th ACM International Conference on Multimedia, pp. 456\u2013465 (2020)","DOI":"10.1145\/3394171.3413752"},{"key":"18_CR16","doi-asserted-by":"crossref","unstructured":"Sawhney, R., Aggarwal, A., Khanna, P., Mathur, P., Jain, T., Shah, R.R.: Risk forecasting from earnings calls acoustics and network correlations. In: INTERSPEECH, pp. 2307\u20132311 (2020)","DOI":"10.21437\/Interspeech.2020-2649"},{"key":"18_CR17","doi-asserted-by":"crossref","unstructured":"Sawhney, R., Aggarwal, A., Shah, R.: An empirical investigation of bias in the multimodal analysis of financial earnings calls. In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 3751\u20133757 (2021)","DOI":"10.18653\/v1\/2021.naacl-main.294"},{"key":"18_CR18","doi-asserted-by":"crossref","unstructured":"Cao, S., Jiang, W., Yang, B., Zhang, A.L.: How to talk when a machine is listening: corporate disclosure in the age of AI. National Bureau of Economic Research, no. w27950 (2020)","DOI":"10.3386\/w27950"},{"key":"18_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2020.101894","volume":"59","author":"D Issa","year":"2020","unstructured":"Issa, D., Demirci, M.F., Yazici, A.: Speech emotion recognition with deep convolutional neural networks. Biomed. Signal Process. Control 59, 101894 (2020)","journal-title":"Biomed. Signal Process. Control"},{"issue":"5","key":"18_CR20","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0196391","volume":"13","author":"SR Livingstone","year":"2018","unstructured":"Livingstone, S.R., Russo, F.A.: The Ryerson audio-visual database of emotional speech and song (RAVDESS): a dynamic, multimodal set of facial and vocal expressions in North American English. PLoS ONE 13(5), e0196391 (2018)","journal-title":"PLoS ONE"},{"key":"18_CR21","doi-asserted-by":"crossref","unstructured":"McFee, B., et al.: Librosa: audio and music signal analysis in python. In: Proceedings of the 14th Python in Science Conference, vol. 8, pp. 18\u201325 (2015)","DOI":"10.25080\/Majora-7b98e3ed-003"},{"key":"18_CR22","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1016\/j.knosys.2013.07.008","volume":"51","author":"P Hajek","year":"2013","unstructured":"Hajek, P., Michalak, K.: Feature selection in corporate credit rating prediction. Knowl.-Based Syst. 51, 72\u201384 (2013)","journal-title":"Knowl.-Based Syst."},{"key":"18_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2019.07.033","volume":"138","author":"H Son","year":"2019","unstructured":"Son, H., Hyun, C., Phan, D., Hwang, H.J.: Data analytic approach for bankruptcy prediction. Expert Syst. Appl. 138, 112816 (2019)","journal-title":"Expert Syst. Appl."},{"issue":"2","key":"18_CR24","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1111\/jifm.12053","volume":"28","author":"EI Altman","year":"2017","unstructured":"Altman, E.I., Iwanicz-Drozdowska, M., Laitinen, E.K., Suvas, A.: Financial distress prediction in an international context: a review and empirical analysis of Altman\u2019s Z-score model. J. Int. Financ. Manag. Account. 28(2), 131\u2013171 (2017)","journal-title":"J. Int. Financ. Manag. Account."},{"key":"18_CR25","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1016\/j.inffus.2019.07.006","volume":"54","author":"J Sun","year":"2020","unstructured":"Sun, J., Li, H., Fujita, H., Fu, B., Ai, W.: Class-imbalanced dynamic financial distress prediction based on Adaboost-SVM ensemble combined with SMOTE and time weighting. Inf. Fusion 54, 128\u2013144 (2020)","journal-title":"Inf. Fusion"},{"key":"18_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2019.105663","volume":"83","author":"YP Huang","year":"2019","unstructured":"Huang, Y.P., Yen, M.F.: A new perspective of performance comparison among machine learning algorithms for financial distress prediction. Appl. Soft Comput. 83, 105663 (2019)","journal-title":"Appl. Soft Comput."},{"issue":"12","key":"18_CR27","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0225989","volume":"14","author":"D Alaminos","year":"2019","unstructured":"Alaminos, D., Fern\u00e1ndez, M.\u00c1.: Why do football clubs fail financially? A financial distress prediction model for European professional football industry. PLoS ONE 14(12), e0225989 (2019)","journal-title":"PLoS ONE"},{"key":"18_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106758","volume":"97","author":"X Du","year":"2020","unstructured":"Du, X., Li, W., Ruan, S., Li, L.: CUS-heterogeneous ensemble-based financial distress prediction for imbalanced dataset with ensemble feature selection. Appl. Soft Comput. 97, 106758 (2020)","journal-title":"Appl. Soft Comput."},{"key":"18_CR29","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1016\/j.jbusres.2020.07.052","volume":"120","author":"D Liang","year":"2020","unstructured":"Liang, D., Tsai, C.F., Lu, H.Y.R., Chang, L.S.: Combining corporate governance indicators with stacking ensembles for financial distress prediction. J. Bus. Res. 120, 137\u2013146 (2020)","journal-title":"J. Bus. Res."},{"key":"18_CR30","unstructured":"Lundberg, S.M., Lee, S.I.: A unified approach to interpreting model predictions. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 4768\u20134777 (2017)"}],"container-title":["IFIP Advances in Information and Communication Technology","Artificial Intelligence Applications and Innovations"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-08333-4_18","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,16]],"date-time":"2022-06-16T16:05:18Z","timestamp":1655395518000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-08333-4_18"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031083327","9783031083334"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-08333-4_18","relation":{},"ISSN":["1868-4238","1868-422X"],"issn-type":[{"value":"1868-4238","type":"print"},{"value":"1868-422X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"10 June 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"AIAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"IFIP International Conference on Artificial Intelligence Applications and Innovations","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hersonissos","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 June 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 June 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aiai2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ifipaiai.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}