Abstract
This paper describes the impact of spoken language variation in a multilingual speaker identification (SID) system. The development of speech technology applications in low resource languages (LRL) is challenging due to the unavailability of proper speech corpus. This paper illustrates an experimental study of SID on Eastern and Northeastern (E&NE) Indian languages in language mismatch conditions. For this purpose, several experiments are carried out using the LRL data to build speaker identification models. Here, spectral features are explored for investigating the presence of speaker-specific information. Mel frequency cepstral coefficients (MFCCs) and shifted delta cepstral (SDC) are used for representing the spectral information. Gaussian mixture model (GMM) and support vector machine (SVM)-based models are developed to represent the speaker-specific information captured through the spectral features. Apart from that, to build the modern SID i-vectors, time delay neural networks (TDNN), and recurrent neural network with long short-term memory (LSTM-RNN) have been considered. For the evaluation, equal error rate (EER) has been used as a performance matrix of the SID system. Performances of the developed systems are analyzed with native and non-native corpus in terms of speaker identification (SID) accuracy. The best SID performances are observed to be EER 10.52% after the corpus fusion mechanism.
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
Reynolds, D.: Comparison of background normalization methods for text-independent speaker verification. In: EUROSPEECH (1997)
Akbacak, M., Hansen, J.H.L.: Language normalization for bilingual speaker recognition systems. In: 2007 IEEE International Conference on Acoustics, Speech and Signal Processing—ICASSP ’07, Vol. 4, pp. IV-257-IV–260 (2007)
Hegde, R.M., Murthy, H.A.: Automatic language identification and discrimination using the modified group delay feature. In: Proceedings of 2005 International Conference on Intelligent Sensing and Information Processing, pp. 395–399 (2005)
Besacier, L., Barnard, E., Karpov, A., Schultz, T.: Automatic speech recognition for under-resourced languages: A survey. Speech Commun. 56, 85–100 (2014)
Baby, A., Thomas, A.N.L., Consortium, T.T.S.: Resources for Indian languages. In: CBBLR—Community Based Building of Language Resources, Brno, Czech Republic: Tribun EU, pp 37–43 (2016)
Campbell, J.P.: Speaker recognition: A tutorial. Proc. IEEE 85(9), 1437–1462 (1997)
Aanchan Mohan, S.U., Rose, R., Ghalehjegh, S.H.: Acoustic modelling for speech recognition in Indian languages in an agricultural commodities task domain. Speech Commun. vol. 56, pp. 167–180 (2014)
Basu, J., Khan, S., Roy, R., Bepari, M.S.: Commodity price retrieval system in Bangla, in: Proceedings of the 11th Asia Pacific Conference on Computer–Human Interaction - APCHI ’13, pp. 406–415 (2013)
Samudravijaya, S., Rao, K., Agrawal, P.V.S.: Hindi speech database. In: Int. Conf. on Spoken Language processing (ICSLP 2000) (2000)
Tang, Z., Wang, D., Chen, Y., Li, L., Abel, A.: Phonetic temporal neural model for language identification. IEEE/ACM Trans. Audio, Speech, Lang. Process. 26(1), 134–144 (2018)
Dehak, N., Kenny, P.J., Dehak, R., Dumouchel, P., Ouellet, P.: Front-end factor analysis for speaker verification. IEEE Trans. Audio. Speech. Lang. Processing 19(4), 788–798 (2011)
Richardson, F., Reynolds, D., Dehak, N.: Deep neural network approaches to speaker and language recognition. IEEE Signal Process. Lett. 22(10), 1671–1675 (2015)
Snyder, D., Garcia-Romero, D., McCree, A., Sell, G., Povey, D., Khudanpur, S.: Spoken language recognition using x-vectors. In: Odyssey, pp. 105–111 (2018)
Manchala, V., Prasad, S., Janaki, V. K.: GMM based language identification system using robust features. Int. J. Speech Technol. 17(2), 99–105 (2014)
Mansour, E., Sayed, M.S., Moselhy, A.M., Abdelnaiem, A.A.: LPC and MFCC performance evaluation with artificial neural network for spoken language identification. Int. J. Signal Process. Image Process. Pattern Recognit. 6(3), 55–66 (2013)
Rabiner, L.R.: A tutorial on hidden Markov models and selected applications in speech recognition. Proc. IEEE 77(2), 257–286 (1989)
Reynolds, D.: Gaussian Mixture Models. In: Li, S.Z., Jain, A.K. (eds.) Encyclopedia of Biometrics, Boston, MA: Springer US, pp. 827–832 (2015)
Campbell, W.M., Sturim, D., Reynolds, D.A.: Support vector machines using GMM supervectors for speaker verification. IEEE Signal Process. Lett. 13(5), 308–311 (2006)
Dey, N.S., Mohanty, R., Chugh, K.L.: Speech and Speaker Recognition System Using Artificial Neural Networks and Hidden Markov Model. Int. Conf. Commun. Syst. Netw. Technol. 2012, 311–315 (2012)
Khan, S., Basu, J., Bepari, M.S.: Performance Evaluation of PBDP Based Real-Time Speaker Identification System with Normal MFCC vs MFCC of LP Residual Features. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7143 LNCS, pp. 358–366 (2012)
Khan, S., Basu, J., Bepari, M.S., Roy, R.: Pitch based selection of optimal search space at runtime: Speaker recognition perspective. In: 2012 4th International Conference on Intelligent Human Computer Interaction (IHCI), pp. 1–6 (2012)
Reynolds, D.A.: Speaker identification and verification using Gaussian mixture speaker models. Speech Commun. 17(1–2), 91–108
Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, New York, New York, NY (2000)
Dehak, N., Dehak, R., Kenny, P., Brümmer, N., Ouellet, P.,Dumouchel, P.: Support vector machines versus fast scoring in the low-dimensional total variability space for speaker verification. In: INTERSPEECH (2009)
Balakrishnama, S., Ganapathiraju, A.: Linear discriminant analysis-a brief tutorial. Inst. Signal Inf. Process. 18, 1–8 (1998)
Prince, S.J.D., Elder, J. H.: Probabilistic linear discriminant analysis for inferences about identity. In: 2007 IEEE 11th International Conference on Computer Vision, pp. 1–8 (2007)
Lang, K.J., Waibel, A.H., Hinton, G.E.: A time-delay neural network architecture for isolated word recognition. Neural Networks 3(1), 23–43 (Jan. 1990)
Hochreiter, S., Schmidhuber, J.: Long Short-Term Memory. Neural Comput. 9(8), 1735–1780 (Nov. 1997)
Acknowledgements
For supporting speech data collection from native speakers of Northeast India, authors would like to acknowledge the North Eastern Regional Institute of Science and Technology (NERIST), Arunachal Pradesh, India. The authors thank to all native speakers of E&NE Indian states for contribution. The authors are also grateful to the Centre for Development of Advanced Computing (CDAC), Kolkata, India for the necessary support to carry out the research activities.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Basu, J., Majumder, S. (2021). Speaker Identification in Spoken Language Mismatch Condition: An Experimental Study. In: Bhattacharyya, S., Mršić, L., Brkljačić, M., Kureethara, J.V., Koeppen, M. (eds) Recent Trends in Signal and Image Processing. ISSIP 2020. Advances in Intelligent Systems and Computing, vol 1333. Springer, Singapore. https://doi.org/10.1007/978-981-33-6966-5_8
Download citation
DOI: https://doi.org/10.1007/978-981-33-6966-5_8
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-33-6965-8
Online ISBN: 978-981-33-6966-5
eBook Packages: EngineeringEngineering (R0)