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Efficient speaker identification using spectral entropy

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Abstract

In voice recognition, the two main problems are speech recognition (what was said), and speaker recognition (who was speaking). The usual method for speaker recognition is to postulate a model where the speaker identity corresponds to the parameters of the model, which estimation could be time-consuming when the number of candidate speakers is large. In this paper, we model the speaker as a high dimensional point cloud of entropy-based features, extracted from the speech signal. The method allows indexing, and hence it can manage large databases. We experimentally assessed the quality of the identification with a publicly available database formed by extracting audio from a collection of YouTube videos of 1,000 different speakers. With 20 second audio excerpts, we were able to identify a speaker with 97% accuracy when the recording environment is not controlled, and with 99% accuracy for controlled recording environments.

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References

  1. Beltrán J, Chávez E, Favela J (2015) Scalable identification of mixed environmental sounds, recorded from heterogeneous sources. Pattern Recogn Lett 68:153–160

    Article  Google Scholar 

  2. Bernhardsson E Annoy: approximate nearest neighbors in C++/Python optimized for memory usage and loading/saving to disk. https://github.com/spotify/annoy

  3. Davis SB, Mermelstein P (1980) Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences. In: IEEE transactions on acoustics, speech, and signal processing, vol 28, pp 357–366

  4. Dehak N, Kenny PJ, Dehak R, Dumouchel P, Ouellet P (2011) Front-end factor analysis for speaker verification. In: IEEE transactions on audio, speech and language processing, vol 19. pp 788–798

  5. Greenberg C, Bansé D (2014) The NIST 2014 speaker recognition i-vector machine learning challenge. In: Proc the speaker and language recognition workshop, pp 224–230

  6. Hansen JH, Hasan T (2015) Speaker recognition by machines and humans: a tutorial review. IEEE Signal Proc Mag 32(6):74–99

    Article  Google Scholar 

  7. Kenny P (2005) Joint factor analysis of speaker and session variability: theory and algorithms. CRIM, Montreal,(Report) CRIM-06/08-13, pp 1–17

  8. Kenny P, Mihoubi M, Dumouchel P (2003) New MAP estimators for speaker recognition. Interspeech, pp 1–4

  9. Kinnunen T, Li H (2010) An overview of text-independent speaker recognition: from features to supervectors. Speech Comm 52:12–40

    Article  Google Scholar 

  10. Kulis B, Grauman K (2012) Kernelized locality-sensitive hashing. IEEE Trans Pattern Anal Mach Intell 34(6):1092–1104

    Article  Google Scholar 

  11. Liu Y, Nie L, Liu L, Rosenblum DS (2016) From action to activity: sensor-based activity recognition. Neurocomputing 181:108–115

    Article  Google Scholar 

  12. Reynolds DA, Quatieri TF, Dunn RB (2000) Speaker verification using adapted Gaussian mixture models. Digital Signal Processing: A Review Journal 10(1):19–41

    Article  Google Scholar 

  13. Schmidt L (2014) Large scale speaker identification. In: 2014 IEEE international conference on acoustic, speech and signal processing (ICASSP), pp 1669–1673

  14. Shannon CE (2001) A mathematical theory of communication. ACM SIGMOBILE Mobile Computing and Communications Review 5(1):3

    Article  MathSciNet  Google Scholar 

  15. Snyder D, Garcia-romero D, Povey D (2015) Time delay deep neural network-based universal background models for speaker recognition. In: 2015 IEEE workshop on automatic speech recognition and understanding (ASRU), IEEE, pp 92–97

  16. Uhlmann JK (1991) Satisfying general proximity / similarity queries with metric trees. Inf Process Lett 40:175–179

    Article  MATH  Google Scholar 

  17. Yianilos PN (1993) Data structures and algorithms for nearest neighbor search in general metric spaces. Annual ACM-SIAM Symposium on Discrete Algorithms, pp 311–321

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Authors and Affiliations

  1. CICESE, Ensenada, Mexico

    Fernando Luque-Suárez & Edgar Chávez

  2. Universidad Michoacana, Morelia, Mexico

    Antonio Camarena-Ibarrola

Authors
  1. Fernando Luque-Suárez
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  2. Antonio Camarena-Ibarrola
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  3. Edgar Chávez
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Correspondence to Fernando Luque-Suárez.

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Luque-Suárez, F., Camarena-Ibarrola, A. & Chávez, E. Efficient speaker identification using spectral entropy. Multimed Tools Appl 78, 16803–16815 (2019). https://doi.org/10.1007/s11042-018-7035-9

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  • DOI: https://doi.org/10.1007/s11042-018-7035-9

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