Abstract
Automatic Speaker Verification (ASV) systems accuracy is based on the spoken language used in training and enrolling speakers. Language dependency makes voice-based security systems less robust and generalizable to a wide range of applications. In this work, a study on language dependency of a speaker verification system and experiments are performed to benchmark the robustness of the x-vector based techniques to language dependency. Experiments are carried out on a smartphone multi-lingual dataset with 50 subjects containing utterances in four different languages captured in five sessions. We have used two world training datasets, one with only one language and one with multiple languages. Results show that performance is degraded when there is a language mismatch in enrolling and testing. Further, our experimental results indicate that the performance degradation depends on the language present in the word training data.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Auckenthaler, R., Carey, M.J., Mason, J.S.: Language dependency in text-independent speaker verification. In: Proceedings of 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing (Cat. No. 01CH37221), vol. 1, pp. 441–444. IEEE (2001)
Chung, J.S., Nagrani, A., Zisserman, A.: VoxCeleb2: deep speaker recognition. In: INTERSPEECH (2018)
Gupta, C.S.: Significance of source features for speaker recognition. Master’s thesis, Indian Institute of Technology Madras, Dept. of Computer Science and Engineering., Chennai, India (2003)
Hébert, M.: Text-dependent speaker recognition. In: Benesty, J., Sondhi, M.M., Huang, Y.A. (eds.) Springer Handbook of Speech Processing. SH, pp. 743–762. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-49127-9_37
ISO/IEC JTC1 SC37 Biometrics: ISO/IEC 19795–1:2006. Information Technology - Biometric Performance Testing and Reporting - Part 1: Principles and Framework. International Organization for Standardization and International Electrotechnical Committee, March 2006
Kinnunen, T., Li, H.: An overview of text-independent speaker recognition: from features to supervectors. Speech Commun. 52(1), 12–40 (2010)
Li, L., Wang, D., Rozi, A., Zheng, T.F.: Cross-lingual speaker verification with deep feature learning. In: 2017 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), pp. 1040–1044. IEEE (2017)
Lu, X., Dang, J.: An investigation of dependencies between frequency components and speaker characteristics for text-independent speaker identification. Speech Commun. 50(4), 312–322 (2008)
Misra, A., Hansen, J.H.L.: Spoken language mismatch in speaker verification: an investigation with NIST-SRE and CRSS bi-ling corpora. In: 2014 IEEE Spoken Language Technology Workshop (SLT), pp. 372–377 (2014)
Murty, K.S.R., Yegnanarayana, B.: Combining evidence from residual phase and MFCC features for speaker recognition. IEEE Signal Process. Lett. 13(1), 52–55 (2006)
Nagrani, A., Chung, J.S., Zisserman, A.: VoxCeleb: a large-scale speaker identification dataset. In: Lacerda, F. (ed.) Interspeech 2017, 18th Annual Conference of the International Speech Communication Association, Stockholm, Sweden, 20–24 August 2017, pp. 2616–2620. ISCA (2017). https://doi.org/10.21437/Interspeech.2017. http://www.isca-speech.org/archive/Interspeech_2017/abstracts/0950.html
Plumpe, M.D., Quatieri, T.F., Reynolds, D.A.: Modeling of the glottal flow derivative waveform with application to speaker identification. IEEE Trans. Speech Audio Process. 7(5), 569–586 (1999)
Raghavendra, R., et al.: Smartphone multi-modal biometric authentication: database and evaluation. arXiv preprint arXiv:1912.02487 (2019)
Reynolds, D.A., Rose, R.C.: Robust text-independent speaker identification using gaussian mixture speaker models. IEEE Trans. Speech Audio Process. 3(1), 72–83 (1995)
Snyder, D., Garcia-Romero, D., Povey, D., Khudanpur, S.: Deep neural network embeddings for text-independent speaker verification. In: Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, vol. 2017, pp. 999–1003, August 2017. www.scopus.com
Snyder, D., Garcia-Romero, D., Sell, G., Povey, D., Khudanpur, S.: X-vectors: robust DNN embeddings for speaker recognition. In: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, vol. 2018, pp. 5329–5333, April 2018. www.scopus.com
Wang, J., Johnson, M.T.: Vocal source features for bilingual speaker identification. In: 2013 IEEE China Summit and International Conference on Signal and Information Processing, pp. 170–173 (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Mandalapu, H., Elbo, T.M., Ramachandra, R., Busch, C. (2021). Cross-lingual Speaker Verification: Evaluation on X-Vector Method. In: Yildirim Yayilgan, S., Bajwa, I.S., Sanfilippo, F. (eds) Intelligent Technologies and Applications. INTAP 2020. Communications in Computer and Information Science, vol 1382. Springer, Cham. https://doi.org/10.1007/978-3-030-71711-7_18
Download citation
DOI: https://doi.org/10.1007/978-3-030-71711-7_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-71710-0
Online ISBN: 978-3-030-71711-7
eBook Packages: Computer ScienceComputer Science (R0)