{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T19:16:30Z","timestamp":1776885390242,"version":"3.51.2"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030867010","type":"print"},{"value":"9783030867027","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-3-030-86702-7_4","type":"book-chapter","created":{"date-parts":[[2021,9,28]],"date-time":"2021-09-28T23:13:48Z","timestamp":1632870828000},"page":"38-48","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Fake Speech Recognition Using Deep Learning"],"prefix":"10.1007","author":[{"given":"Steven","family":"Camacho","sequence":"first","affiliation":[]},{"given":"Dora Maria","family":"Ballesteros","sequence":"additional","affiliation":[]},{"given":"Diego","family":"Renza","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,9,29]]},"reference":[{"issue":"2","key":"4_CR1","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1016\/j.bushor.2019.11.006","volume":"63","author":"J Kietzmann","year":"2020","unstructured":"Kietzmann, J., Lee, L.W., McCarthy, I.P., Kietzmann, T.C.: DeepFakes: trick or treat? Bus. Horiz. 63(2), 135\u2013146 (2020)","journal-title":"Bus. Horiz."},{"key":"4_CR2","unstructured":"Paris, B., Donovan, J.: Deepfakes and cheap fakes. Data Soc. 47 (2019)"},{"key":"4_CR3","doi-asserted-by":"crossref","unstructured":"Ahmed, S.: Who inadvertently shares deepfakes? Analyzing the role of political interest, cognitive ability, and social network size. Telemat. Inf. 57, 101508 (2021)","DOI":"10.1016\/j.tele.2020.101508"},{"key":"4_CR4","doi-asserted-by":"crossref","unstructured":"Lieto, A., et al.: Hello? Who am i talking to? A shallow CNN approach for human vs. bot speech classification. In: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, vol. 2019, pp. 2577\u20132581 (2019)","DOI":"10.1109\/ICASSP.2019.8682743"},{"key":"4_CR5","doi-asserted-by":"crossref","unstructured":"Yu, P., Xia, Z., Fei, J., Lu, Y.: A survey on deepfake video detection. IET Biomet. (2021)","DOI":"10.1049\/bme2.12031"},{"key":"4_CR6","doi-asserted-by":"crossref","unstructured":"Guera, D., Delp, E.J.: Deepfake video detection using recurrent neural networks. In: Proceedings of AVSS 2018\u20132018 15th IEEE International Conference on Advanced Video and Signal-Based Surveillance, pp. 1\u20136 (2019)","DOI":"10.1109\/AVSS.2018.8639163"},{"key":"4_CR7","unstructured":"Dolhansky, B., Bitton, J., Pflaum, B., Lu, J., Howes, R., Wang, M., Ferrer, C.C.: The deepfake detection challenge dataset. arXiv preprint arXiv:2006.07397 (2020)"},{"key":"4_CR8","doi-asserted-by":"crossref","unstructured":"Lyu, S.: Deepfake detection: Current challenges and next steps, pp. 1\u20136 (2020)","DOI":"10.1109\/ICMEW46912.2020.9105991"},{"key":"4_CR9","unstructured":"Nguyen, T.T., Nguyen, C.M., Nguyen, D.T., Nguyen, D.T., Nahavandi, S.: Deep Learning for Deepfakes Creation and Detection: A Survey, pp. 1\u201312 (2019)"},{"key":"4_CR10","unstructured":"van den Oord, A., et al.: WaveNet: A Generative Model for Raw Audio, pp. 1\u201315 (2016)"},{"key":"4_CR11","doi-asserted-by":"crossref","unstructured":"Elias, I., et al.: Parallel Tacotron 2: A Non-Autoregressive Neural TTS Model with Differentiable Duration Modeling (2021)","DOI":"10.21437\/Interspeech.2021-1461"},{"key":"4_CR12","doi-asserted-by":"publisher","first-page":"347","DOI":"10.1016\/j.csl.2019.05.008","volume":"58","author":"Y Saito","year":"2019","unstructured":"Saito, Y., Takamichi, S., Saruwatari, H.: Vocoder-free text-to-speech synthesis incorporating generative adversarial networks using low-\/multi-frequency STFT amplitude spectra. Comput. Speech Lang. 58, 347\u2013363 (2019)","journal-title":"Comput. Speech Lang."},{"key":"4_CR13","unstructured":"Arik, S., et al.: Deep voice: real-time neural text-to-speech. In: 34th International Conference on Machine Learning, ICML 2017, vol. 1, pp. 264\u2013273 (2017)"},{"key":"4_CR14","unstructured":"Arik, S.O., et al.: Deep voice 2: multi-speaker neural text-to-speech. In: Advances in Neural Information Processing Systems, vol. 2017, pp. 2963\u20132971 (2017)"},{"key":"4_CR15","unstructured":"Ping, W., et al.: Deep voice 3: scaling text-to-speech with convolutional sequence learning. In: 6th International Conference on Learning Representations, ICLR 2018 - Conference Track Proceedings, pp. 1\u201316 (2018)"},{"key":"4_CR16","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1016\/j.cogsys.2019.09.009","volume":"59","author":"X Zhu","year":"2020","unstructured":"Zhu, X., Xue, L.: Building a controllable expressive speech synthesis system with multiple emotion strengths. Cogn. Syst. Res. 59, 151\u2013159 (2020)","journal-title":"Cogn. Syst. Res."},{"key":"4_CR17","doi-asserted-by":"crossref","unstructured":"Maiti, S., Marchi, E., Conkie, A.: Generating multilingual voices using speaker space translation based on bilingual speaker data. In: ICASSP 2020\u20132020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 7624\u20137628. IEEE (2020)","DOI":"10.1109\/ICASSP40776.2020.9054305"},{"key":"4_CR18","unstructured":"Zhao, Y., et al.: Voice conversion challenge 2020: intra-lingual semi-parallel and cross-lingual voice conversion. arXiv preprint arXiv:2008.12527 (2020)"},{"key":"4_CR19","unstructured":"Sisman, B., Yamagishi, J., Member, S., King, S.: An Overview of Voice Conversion and its Challenges: From Statistical Modeling to Deep Learning, pp. 1\u201327 (2008)"},{"key":"4_CR20","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1016\/j.specom.2017.01.008","volume":"88","author":"SH Mohammadi","year":"2017","unstructured":"Mohammadi, S.H., Kain, A.: An overview of voice conversion systems. Speech Commun. 88, 65\u201382 (2017)","journal-title":"Speech Commun."},{"key":"4_CR21","unstructured":"Canton, C., Brian Dolhansky, J.B., Ben Pflaum, J.P., Lu, J.: Deepfake detection challenge results: An open initiative to advance AI, June 2020https:\/\/ai.facebook.com\/blog\/deepfake-detection-challenge-results-an-open-initiative-to-advance-ai\/"},{"key":"4_CR22","unstructured":"H\u00e9ctor, N., Tomi, K., Xuechen, A., Jose, M.S., Massimiliano, X.W., Junichi. ASVSPOOF 2021: Automatic speaker verification spoofing and countermeasures challenge evaluation plan (2021)"},{"key":"4_CR23","doi-asserted-by":"crossref","unstructured":"Reimao, R., Tzerpos, V.: FoR: a dataset for synthetic speech detection. In: 2019 10th International Conference on Speech Technology and Human-Computer Dialogue, SpeD 2019 (2019)","DOI":"10.1109\/SPED.2019.8906599"},{"key":"4_CR24","doi-asserted-by":"crossref","unstructured":"Ballesteros, D.M., Rodriguez, Y., Renza, D.: A dataset of histograms of original and fake voice recordings (h-voice). Data Brief 29, 105331 (2020)","DOI":"10.1016\/j.dib.2020.105331"},{"key":"4_CR25","unstructured":"Rodriguez, Y., Ballesteros, D.M., Renza, S.: Fake voice recordings (imitation), November 2019"},{"key":"4_CR26","doi-asserted-by":"crossref","unstructured":"Wang, R., et al.: DeepSonar: Towards Effective and Robust Detection of AI-Synthesized Fake Voices (2020)","DOI":"10.1145\/3394171.3413716"},{"key":"4_CR27","unstructured":"AlBadawy, E.A., Lyu, S., Farid, H.: Detecting AI-synthesized speech using bispectral analysis. In: CVPR Workshops, pp. 104\u2013109 (2019)"},{"key":"4_CR28","doi-asserted-by":"crossref","unstructured":"Chen, T., Kumar, A., Nagarsheth, P., Sivaraman, G., Khoury, E.: Generalization of audio deepfake detection. In: Proceedings of the Odyssey Speaker and Language Recognition Workshop, Tokyo, Japan, pp. 1\u20135 (2020)","DOI":"10.21437\/Odyssey.2020-19"},{"key":"4_CR29","doi-asserted-by":"crossref","unstructured":"Gao, Y., Vuong, T., Elyasi, M., Bharaj, G., Singh, R., et al.: Generalized spoofing detection inspired from audio generation artifacts. arXiv preprint arXiv:2104.04111 (2021)","DOI":"10.21437\/Interspeech.2021-1705"},{"key":"4_CR30","doi-asserted-by":"crossref","unstructured":"Ballesteros, D.M., Rodriguez-Ortega, Y., Renza, D., Arce, G.: Deep4SNet: deep learning for fake speech classification. Expert Syst. Appl. 184, 115465 (2021)","DOI":"10.1016\/j.eswa.2021.115465"},{"key":"4_CR31","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-61702-8_1","volume-title":"Applied Informatics","author":"Y Rodr\u00edguez-Ortega","year":"2020","unstructured":"Rodr\u00edguez-Ortega, Y., Ballesteros, D.M., Renza, D.: A machine learning model to detect fake voice. In: Florez, H., Misra, S. (eds.) ICAI 2020. CCIS, vol. 1277, pp. 3\u201313. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-61702-8_1"}],"container-title":["Communications in Computer and Information Science","Applied Computer Sciences in Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-86702-7_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,28]],"date-time":"2021-09-28T23:15:46Z","timestamp":1632870946000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-86702-7_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030867010","9783030867027"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-86702-7_4","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"29 September 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"WEA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Workshop on Engineering Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Medell\u00edn","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Colombia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 October 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"woea2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ieee.udistrital.edu.co\/wea2021","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"127","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"33","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"11","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"26% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.73","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.54","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Due to the COVID-19 pandemic the conference was held in a hybrid mode.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}