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Search for speaker identity in historical oral archives

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Abstract

We present our ongoing research focused on speaker recognition in historical oral archives. This research is part of our long-term effort aimed at enabling versatile access to the archive of the Czech Radio (CRo). Based on a manually annotated partition of the archive, we compiled a database covering a time span of more than 30 years to carry out our experimental study. Hence we were able to investigate the impact of various aspects that make it challenging to process historical data. We show the shift of scores for target (genuine) speaker trials introduced by the aging effect, the value of the signal-to-noise ratio or by the variable amount of the enrollment and test data. Scores for speaker detection trials were assessed by a system based on the i-vector paradigm and probabilistic linear discriminative analysis. We also assessed the performance of this system using an evaluation database containing contemporary recordings collected over a time span of approximately 4 years. Although using state-of-the-art techniques, capable of dealing with nuisance inter-session variability, we demonstrate remarkable degradation in the performance of the system in the evaluation containing historical data compared to the one containing contemporary data only. Specifically, the Equal Error Rate (EER) of the system rose to 8.27 % from 1.93 %. The revealed difference thus exemplifies that compensation techniques need to be employed to cope with additional variability introduced in the historical data by various sources.

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Notes

  1. The original name of the company was the Radiojournal company.

  2. http://www.nist.gov/itl/iad/mig/sre.cfm

  3. In [19], the nuisance variability is expressed as sum of an intra-speaker variability confined to a lower-dimensional subspace and a residual noise which is assumed to be Gaussian with a diagonal covariance matrix. A full covariance matrix used in our case is thus simply a generalization of the model.

  4. We used relevance factor of 16.0 in our experiments.

  5. We used the Bosaris toolkit available at https://sites.google.com/site/bosaristoolkit/ to plot our DET curves.

  6. Please note that the results presented in [23] and in this work are not directly comparable. In [23] we used the test database in a two-fold cross-validation setup with one fold used for calibration training and the second for testing, and vice versa. Here we pooled all the test data together. Furthermore, different development data sets were used in [23] and in this work. In the former study, the available data was much more limited, particularly for estimation of intra-speaker variability, requiring recordings from multiple sessions per speaker.

  7. Let us stress that we strictly distinguished between different excerpts and different sessions. Hence, no model was trained for a speaker having multiple excerpts available but they were all drawn from a single session.

  8. The linear regression fit curves displayed in the figure are not intended to represent a true dependency but just its trend.

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Acknowledgments

This research work was supported by the Czech Ministry of Culture (project no. DF11P01OVV013 in program NAKI).

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  1. Technical University of Liberec, Studentska 1402/2, 461 17, Liberec, Czech Republic

    Jan Silovsky, Jan Nouza & Michaela Kucharova

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  1. Jan Silovsky
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  2. Jan Nouza
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  3. Michaela Kucharova
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Correspondence to Jan Silovsky.

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Silovsky, J., Nouza, J. & Kucharova, M. Search for speaker identity in historical oral archives. Multimed Tools Appl 75, 3767–3786 (2016). https://doi.org/10.1007/s11042-014-2067-2

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  • DOI: https://doi.org/10.1007/s11042-014-2067-2

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