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Performance Evaluation of Speaker Identification in Language and Emotion Mismatch Conditions on Eastern and North Eastern Low Resource Languages of India

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Data Engineering for Smart Systems

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 238))

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Abstract

This paper describes the impact of spoken language and emotional 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 performance analysis of SID in six Eastern and North Eastern (E&NE) Indian languages and an emotional corpus of six basic emotions. For this purpose, six experimentations are carried out using the collected LRL of E&NE data to build speaker identification models. Speaker-specific acoustic characteristics are extracted from the speech segments in terms of short-term spectral features, i.e., shifted delta cepstral (SDC) and partial correlation (PARCOR) coefficients. 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 different emotional native and non-native language corpus in terms of speaker identification (SID) accuracy in six different experiments.

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References

  1. Reynolds D (1997) Comparison of background normalization methods for text-independent speaker verification

    Google Scholar 

  2. Akbacak M, Hansen JHL (2007) 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. https://doi.org/10.1109/ICASSP.2007.366898

  3. Misra A, Hansen JHL (2014) 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. https://doi.org/10.1109/SLT.2014.7078603

  4. Koolagudi SG, Fatima SE, Rao KS (2012) Speaker recognition in the case of emotional environment using transformation of speech features. In: Proceedings of the CUBE international information technology conference on - CUBE ’12, p 118. https://doi.org/10.1145/2381716.2381739

  5. Jawarkar NP, Holambe RS, Basu TK (2012) Text-independent speaker identification in emotional environments: a classifier fusion approach. In: Sambath S (ed) Advances in intelligent and soft computing. Springer, Berlin, Heidelberg, pp 569–576

    Google Scholar 

  6. Meftah AH, Mathkour H, Kerrache S, Alotaibi YA (2020) Speaker identification in different emotional states in Arabic and English. IEEE Access 8:60070–60083. https://doi.org/10.1109/ACCESS.2020.2983029

  7. Hegde RM, Murthy HA (2005) 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. https://doi.org/10.1109/ICISIP.2005.1529484

  8. Besacier L, Barnard E, Karpov A, Schultz T (2014) Automatic speech recognition for under-resourced languages: asurvey. Speech Commun 56:85–100. https://doi.org/10.1016/j.specom.2013.07.008

  9. Baby A, Thomas A, Consortium TTS (2016) Resources for Indian languages. CBBLR – Community Based Building of Language Resources, Brno, Czech Republic: Tribun EU, pp 37–43

    Google Scholar 

  10. Campbell JP (1997) Speaker recognition: a tutorial. Proc IEEE 85(9):1437–1462. https://doi.org/10.1109/5.628714

    Article  Google Scholar 

  11. Aanchan Mohan SU, Rose R, Ghalehjegh SH (2014) Acoustic modelling for speech recognition in Indian languages in an agricultural com-modities task domain. Speech Commun 56:167–180

    Google Scholar 

  12. Basu J, Khan S, Roy R, Bepari MS (2013) Commodity price retrieval system in Bangla. In: Proceedings of the 11th Asia Pacific conference on computer human interaction - APCHI ’13, pp 406–415. https://doi.org/10.1145/2525194.2525310

  13. Tang Z, Wang D, Chen Y, Li L, Abel A (2018) Phonetic temporal neural model for language identification. In: IEEE/ACM Trans Audio Speech Lang Process 26(1):134–144. https://doi.org/10.1109/TASLP.2017.2764271

  14. Dehak N, Kenny PJ, Dehak R, Dumouchel P, Ouellet P (2011) Front-end factor analysis for speaker verification. IEEE Trans Audio Speech Lang Process 19(4):788–798. https://doi.org/10.1109/TASL.2010.2064307

  15. Richardson F, Reynolds D, Dehak N (2015) Deep neural network approaches to speaker and language recognition. IEEE Signal Process Lett 22(10):1671–1675. https://doi.org/10.1109/LSP.2015.2420092

  16. Snyder D, Garcia-Romero D, McCree A, Sell G, Povey D, Khudanpur S (2018) Spoken language recognition using x-vectors. In: Odyssey, pp 105–111

    Google Scholar 

  17. Manchala V, Prasad S, Janaki VK (2014) GMM based language identification system using robust features. Int J Speech Technol 17(2):99–105. https://doi.org/10.1007/s10772-013-9209-1

  18. Mansour E, Sayed MS, Moselhy AM, Abdelnaiem AA (2013) LPC and MFCC performance evaluation with artificial neural network for spoken language identification. Int J Signal Process Image Process Patt Recognit 6(3):55–66. 10.1.1.360.2692

    Google Scholar 

  19. Rabiner LR (1989) A tutorial on hidden Markov models and selected applications in speech recognition. Proc IEEE 77(2):257–286. https://doi.org/10.1109/5.18626

  20. Reynolds D (2015) Gaussian mixture models. In: Li SZ, Jain AK (ed) Encyclopedia of biometrics. Springer, Boston, pp 827–832

    Google Scholar 

  21. Campbell WM, Sturim D, Reynolds DA (2006) Support vector machines using GMM supervectors for speaker verification. IEEE Signal Process Lett 13(5):308–311. https://doi.org/10.1109/LSP.2006.870086

  22. Dey NS, Mohanty R, Chugh KL (2012) Speech and speaker recognition system using artificial neural networks and hidden Markov model. In: 2012 international conference on communication systems and network technologies, pp 311–315. https://doi.org/10.1109/CSNT.2012.221

  23. Khan S, Basu J, Bepari MS (2012) 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

    Google Scholar 

  24. Khan S, Basu J, Bepari MS, Roy R (2012) 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. https://doi.org/10.1109/IHCI.2012.6481822

  25. Povey D et al (2011) The Kaldi speech recognition Toolkit

    Google Scholar 

  26. Python. https://www.python.org/

  27. Soni T, Zeidler JR, Ku WH (1995) Behavior of the partial correlation coefficients of a least squares lattice filter in the presence of a nonstationary chirp input. IEEE Trans Signal Process 43(4):852–863. https://doi.org/10.1109/78.376838

  28. Reynolds DA Speaker identification and verification using Gaussian mixture speaker models. Speech Commun 17(1–2):91–108

    Google Scholar 

  29. Vapnik VN (2000) The nature of statistical learning theory. Springer, New York

    Google Scholar 

  30. Dehak N, Dehak R, Kenny P, Brümmer N, Ouellet P, Dumouchel P (2009) Support vector machines versus fast scoring in the low-dimensional total variability space for speaker verification

    Google Scholar 

  31. Balakrishnama S, Ganapathiraju A (1998) Linear discriminant analysis-a brief tutorial. Inst Signal Inf Process 18:1–8

    Google Scholar 

  32. Prince SJD, Elder JH (2007) Probabilistic linear discriminant analysis for inferences about identity. In: 2007 IEEE 11th international conference on computer vision, pp 1–8. https://doi.org/10.1109/ICCV.2007.4409052

  33. Lang KJ, Waibel AH, Hinton GE (1990) A time-delay neural network architecture for isolated word recognition. Neural Netw 3(1):23–43. https://doi.org/10.1016/0893-6080(90)90044-L

    Article  Google Scholar 

  34. Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735

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Acknowledgements

For supporting speech data collection from native speakers of North-East India, authors would like to acknowledge the North Eastern Regional Institute of Science and Technology (NERIST), Arunachal Pradesh, India. The authors thank all native speakers of E&NE Indian states for their 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.

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Authors and Affiliations

  1. CDAC Kolkata, Salt Lake, Sector-V, Kolkata, 700091, India

    Joyanta Basu

  2. Department of Electrical Engineering, IIT, Kharagpur, West Bengal, 721302, India

    Tapan Kumar Basu

  3. Department of Information Technology, Tripura University, Tripura, 799022, India

    Swanirbhar Majumder

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  1. Joyanta Basu
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  2. Tapan Kumar Basu
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  3. Swanirbhar Majumder
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Correspondence to Joyanta Basu .

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Editors and Affiliations

  1. School of Electrical and Data Engineering, University of Technology Sydney, Sydney, NSW, Australia

    Priyadarsi Nanda

  2. Manipal University Jaipur, Jaipur, Rajasthan, India

    Vivek Kumar Verma

  3. Manipal University Jaipur, Jaipur, Rajasthan, India

    Sumit Srivastava

  4. Manipal University Jaipur, Jaipur, Rajasthan, India

    Rohit Kumar Gupta

  5. Department of Computer Science and Engineering, Malviya National Institute of Technology, Jaipur, Rajasthan, India

    Arka Prokash Mazumdar

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Basu, J., Basu, T.K., Majumder, S. (2022). Performance Evaluation of Speaker Identification in Language and Emotion Mismatch Conditions on Eastern and North Eastern Low Resource Languages of India. In: Nanda, P., Verma, V.K., Srivastava, S., Gupta, R.K., Mazumdar, A.P. (eds) Data Engineering for Smart Systems. Lecture Notes in Networks and Systems, vol 238. Springer, Singapore. https://doi.org/10.1007/978-981-16-2641-8_49

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