{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T16:15:06Z","timestamp":1790093706674,"version":"4.0.1"},"reference-count":37,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2022,1,29]],"date-time":"2022-01-29T00:00:00Z","timestamp":1643414400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62071302"],"award-info":[{"award-number":["62071302"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61701306"],"award-info":[{"award-number":["61701306"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>With the rapid development of intelligent speech technologies, automatic speaker verification (ASV) has become one of the most natural and convenient biometric speaker recognition approaches. However, most state-of-the-art ASV systems are vulnerable to spoofing attack techniques, such as speech synthesis, voice conversion, and replay speech. Due to the symmetry distribution characteristic between the genuine (true) speech and spoof (fake) speech pair, the spoofing attack detection is challenging. Many recent research works have been focusing on the ASV anti-spoofing solutions. This work investigates two types of new acoustic features to improve the performance of spoofing attacks. The first features consist of two cepstral coefficients and one LogSpec feature, which are extracted from the linear prediction (LP) residual signals. The second feature is a harmonic and noise subband ratio feature, which can reflect the interaction movement difference of the vocal tract and glottal airflow of the genuine and spoofing speech. The significance of these new features has been investigated in both the t-stochastic neighborhood embedding space and the binary classification modeling space. Experiments on the ASVspoof 2019 database show that the proposed residual features can achieve from 7% to 51.7% relative equal error rate (EER) reduction on the development and evaluation set over the best single system baseline. Furthermore, more than 31.2% relative EER reduction on both the development and evaluation set shows that the proposed new features contain large information complementary to the source acoustic features.<\/jats:p>","DOI":"10.3390\/sym14020274","type":"journal-article","created":{"date-parts":[[2022,1,30]],"date-time":"2022-01-30T00:12:56Z","timestamp":1643501576000},"page":"274","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["New Acoustic Features for Synthetic and Replay Spoofing Attack Detection"],"prefix":"10.3390","volume":"14","author":[{"given":"Linqiang","family":"Wei","sequence":"first","affiliation":[{"name":"Key Innovation Group of Digital Humanities Resource and Research, Shanghai Normal University, Shanghai 200234, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0924-408X","authenticated-orcid":false,"given":"Yanhua","family":"Long","sequence":"additional","affiliation":[{"name":"Key Innovation Group of Digital Humanities Resource and Research, Shanghai Normal University, Shanghai 200234, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0666-5551","authenticated-orcid":false,"given":"Haoran","family":"Wei","sequence":"additional","affiliation":[{"name":"Department of ECE, University of Texas at Dallas, Richardson, TX 75080, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yijie","family":"Li","sequence":"additional","affiliation":[{"name":"Unisound AI Technology Co., Ltd., Beijing 100096, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1016\/j.specom.2014.10.005","article-title":"Spoofing and countermeasures for speaker verification","volume":"66","author":"Wu","year":"2015","journal-title":"Speech Commun."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"101114","DOI":"10.1016\/j.csl.2020.101114","article-title":"ASVspoof 2019: A large-scale public database of synthetized, converted and replayed speech","volume":"64","author":"Wang","year":"2020","journal-title":"Comput. Speech Lang."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Valizada, A., Jafarova, S., Sultanov, E., and Rustamov, S. (2021). Development and Evaluation of Speech Synthesis System Based on Deep Learning Models. Symmetry, 13.","DOI":"10.3390\/sym13050819"},{"key":"ref_4","unstructured":"Stylianou, Y. (1996). Harmonic plus Noise Models for Speech, Combined with Statistical Methods, for Speech and Speaker Modification. [Ph.D. Thesis, Ecole Nationale Superieure des Telecommunications]."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Wu, Z., Kinnunen, T., Evans, N., Yamagishi, J., Hanil\u00e7i, C., Sahidullah, M., and Sizov, A. (2015, January 6\u201310). ASVspoof 2015: The first automatic speaker verification spoofing and countermeasures challenge. Proceedings of the 16th Annual Conference of the International Speech Communication Association (Interspeech), Dresden, Germany.","DOI":"10.21437\/Interspeech.2015-462"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Kinnunen, T., Sahidullah, M., Delgado, H., Todisco, M., Evans, N., Yamagishi, J., and Lee, K.A. (2017, January 20\u201324). The ASVspoof 2017 challenge: Assessing the limits of replay spoofing attack detection. Proceedings of the 18th Annual Conference of the International Speech Communication Association (Interspeech), Stockholm, Sweden.","DOI":"10.21437\/Interspeech.2017-1111"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Wang, L., Yoshida, Y., Kawakami, Y., and Nakagawa, S. (2015, January 6\u201310). Relative phase information for detecting human speech and spoofed speech. Proceedings of the 16th Annual Conference of the International Speech Communication Association (Interspeech), Dresden, Germany.","DOI":"10.21437\/Interspeech.2015-473"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/j.csl.2017.10.001","article-title":"Synthetic speech detection using fundamental frequency variation and spectral features","volume":"48","author":"Pal","year":"2018","journal-title":"Comput. Speech Lang."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"516","DOI":"10.1016\/j.csl.2017.01.001","article-title":"Constant Q cepstral coefficients: A spoofing countermeasure for automatic speaker verification","volume":"45","author":"Todisco","year":"2017","journal-title":"Comput. Speech Lang."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Todisco, M., Wang, X., Vestman, V., Sahidullah, M., Delgado, H., Nautsch, A., Yamagishi, J., Evans, N., Kinnunen, T., and Lee, K.A. (2019, January 15\u201319). ASVspoof 2019: Future Horizons in Spoofed and Fake Audio Detection. Proceedings of the 20th Annual Conference of the International Speech Communication Association (Interspeech), Graz, Austria.","DOI":"10.21437\/Interspeech.2019-2249"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Alam, J. (2019). On the use of fisher vector encoding for voice spoofing detection. Proceedings, 31.","DOI":"10.3390\/proceedings2019031037"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Jelil, S., Das, R.K., Prasanna, S.M., and Sinha, R. (2017, January 20\u201324). Spoof detection using source, instantaneous frequency and cepstral features. Proceedings of the Interspeech 2017, Stockholm, Sweden.","DOI":"10.21437\/Interspeech.2017-930"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Villalba, J., Miguel, A., Ortega, A., and Lleida, E. (2015, January 6\u201310). Spoofing detection with DNN and one-class SVM for the ASVspoof 2015 challenge. Proceedings of the 16th Annual Conference of the International Speech Communication Association (Interspeech), Dresden, Germany.","DOI":"10.21437\/Interspeech.2015-468"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Nagarsheth, P., Khoury, E., Patil, K., and Garland, M. (2017, January 20\u201324). Replay attack detection using DNN for channel discrimination. Proceedings of the 18th Annual Conference of the International Speech Communication Association (Interspeech), Stockholm, Sweden.","DOI":"10.21437\/Interspeech.2017-1377"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"4633","DOI":"10.1109\/TNNLS.2017.2771947","article-title":"Spoofing detection in automatic speaker verification systems using DNN classifiers and dynamic acoustic features","volume":"29","author":"Yu","year":"2017","journal-title":"IEEE Trans. Neural Networks Learn. Syst."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"684","DOI":"10.1109\/JSTSP.2016.2647199","article-title":"An investigation of deep-learning frameworks for speaker verification antispoofing","volume":"11","author":"Zhang","year":"2017","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Chen, Z., Zhang, W., Xie, Z., Xu, X., and Chen, D. (2018, January 15\u201320). Recurrent neural networks for automatic replay spoofing attack detection. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, AB, Canada.","DOI":"10.1109\/ICASSP.2018.8462644"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Lavrentyeva, G., Novoselov, S., Malykh, E., Kozlov, A., Kudashev, O., and Shchemelinin, V. (2017, January 20\u201324). Audio replay attack detection with deep learning frameworks. Proceedings of the 18th Annual Conference of the International Speech Communication Association (Interspeech), Stockholm, Sweden.","DOI":"10.21437\/Interspeech.2017-360"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Tak, H., and Patil, H.A. (2018, January 2\u20136). Novel linear frequency residual cepstral features for replay attack detection. Proceedings of the Interspeech 2018, Hyderabad, India.","DOI":"10.21437\/Interspeech.2018-1702"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Feng, Z., Tong, Q., Long, Y., Wei, S., Yang, C., and Zhang, Q. (2019, January 18\u201321). SHNU Anti-spoofing Systems for ASVspoof 2019 Challenge. Proceedings of the IEEE Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), Lanzhou, China.","DOI":"10.1109\/APSIPAASC47483.2019.9023319"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Witkowski, M., Kacprzak, S., Zelasko, P., Kowalczyk, K., and Ga\u0142ka, J. (2017, January 20\u201324). Audio replay attack detection using high-frequency features. Proceedings of the 18th Annual Conference of the International Speech Communication Association (Interspeech), Stockholm, Sweden.","DOI":"10.21437\/Interspeech.2017-776"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Cheng, X., Xu, M., and Zheng, T. (2019, January 18\u201321). Replay detection using CQT-based modified group delay feature and ResNeWt network in ASVspoof 2019. Proceedings of the IEEE Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), Lanzhou, China.","DOI":"10.1109\/APSIPAASC47483.2019.9023158"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"152837","DOI":"10.1016\/j.aeue.2019.152837","article-title":"Usefulness of linear prediction residual for replay attack detection","volume":"110","author":"Singh","year":"2019","journal-title":"Int. J. Electron. Commun."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.dsp.2019.02.018","article-title":"Low frequency frame-wise normalization over constant-Q transform for playback speech detetion","volume":"89","author":"Yang","year":"2019","journal-title":"Digit. Signal Process."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Tom, M.F., and Dey, P. (2018, January 15\u201319). End-to-end audio replay attack detection using deep convolutional networks with attention. Proceedings of the 19th Annual Conference of the International Speech Communication Association (Interspeech), Graz, Austria.","DOI":"10.21437\/Interspeech.2018-2279"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Sriskandaraja, K., Sethu, V., and Ambikairajah, E. (2018, January 15\u201319). Deep Siamese architecture based replay detection for secure voice biometric. Proceedings of the 19th Annual Conference of the International Speech Communication Association (Interspeech), Graz, Austria.","DOI":"10.21437\/Interspeech.2018-1819"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.specom.2016.10.007","article-title":"Deep features for automatic spoofing detection","volume":"85","author":"Qian","year":"2016","journal-title":"Speech Commun."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Wu, Z., Xiao, X., Chng, E.S., and Li, H. (2013, January 26\u201331). Synthetic speech detection using temporal modulation feature. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Vancouver, BC, Canada.","DOI":"10.1109\/ICASSP.2013.6639067"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"618","DOI":"10.1109\/JSTSP.2016.2647201","article-title":"Cochlear filter and instantaneous frequency based features for spoofed speech detection","volume":"11","author":"Patel","year":"2016","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Sahidullah, M., Kinnunen, T., and Hanilci, C. (2015, January 6\u201310). A comparison of features for synthetic speech detection. Proceedings of the 16th Annual Conference of the International Speech Communication Association (Interspeech), Dresden, Germany.","DOI":"10.21437\/Interspeech.2015-472"},{"key":"ref_31","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Long, Y., Yan, Z.J., Soong, F.K., Dai, L., and Guo, W. (2011, January 22\u201327). Speaker characterization using spectral subband energy ratio based on harmonic plus noise model. Proceedings of the 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Prague, Czech Republic.","DOI":"10.1109\/ICASSP.2011.5947359"},{"key":"ref_33","unstructured":"Povey, D., Ghoshal, A., Boulianne, G., Burget, L., Glembek, O., Goel, N., Hannemann, M., Motlicek, P., Qian, Y., and Schwarz, P. (2011, January 11\u201315). The Kaldi speech recognition toolkit. Proceedings of the 2011 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU), Waikoloa, HI, USA."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1109\/TASSP.1976.1162846","article-title":"A comparative performance study of several pitch detection algorithms","volume":"24","author":"Rabiner","year":"2003","journal-title":"IEEE Trans. Acoust. Speech Signal Process."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2202","DOI":"10.1121\/1.418204","article-title":"Psychoacoustical evaluation of the pitch-synchronous overlap-and-add speech-waveform manipulation technique using single-format stimuli","volume":"101","author":"Reinier","year":"1997","journal-title":"J. Acoust. Soc. Am."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Kinnunen, T., Lee, K.A., Delgado, H., Evans, N., Todisco, M., Sahidullah, M., Yamagishi, J., and Reynolds, D.A. (2018). t-DCF: A detection cost function for the tandem assessment of spoofing countermeasures and automatic speaker verification. arXiv.","DOI":"10.21437\/Odyssey.2018-44"},{"key":"ref_37","unstructured":"Br\u00fcmmer, N., and De Villiers, E. (2013). The bosaris toolkit: Theory, algorithms and code for surviving the new dcf. arXiv."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/14\/2\/274\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:11:12Z","timestamp":1760134272000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/14\/2\/274"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,29]]},"references-count":37,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2022,2]]}},"alternative-id":["sym14020274"],"URL":"https:\/\/doi.org\/10.3390\/sym14020274","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,29]]}}}