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Two-space variability compensation technique for speaker verification in short length and reverberant environments

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Abstract

The performance of state-of-the-art speaker verification in uncontrolled environment is affected by different variabilities. Short duration variability is very common in these scenarios and causes the speaker verification performance to decrease quickly while the duration of verification utterances decreases. Linear discriminant analysis (LDA) is the most common session variability compensation algorithm, nevertheless it presents some shortcomings when trained with insufficient data. In this paper we introduce two methods for session variability compensation to deal with short-length utterances on i-vector space. The first method proposes to incorporate the short duration variability information in the within-class variance estimation process. The second proposes to compensate the session and short duration variabilities in two different spaces with LDA algorithms (2S-LDA). First, we analyzed the behavior of the within and between class scatters in the first proposed method. Then, both proposed methods are evaluated on telephone session from NIST SRE-08 for different duration of the evaluation utterances: full (average 2.5 min), 20, 15, 10 and 5 s. The 2S-LDA method obtains good results on different short-length utterances conditions in the evaluations, with a EER relative average improvement of 1.58%, compared to the best baseline (WCCN[LDA]). Finally, we applied the 2S-LDA method in speaker verification under reverberant environment, using different reverberant conditions from Reverb challenge 2013, obtaining an improvement of 8.96 and 23% under matched and mismatched reverberant conditions, respectively.

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Notes

  1. The standard speaker verification method based on LDA session compensation and PLDA model as classifier.

  2. The term “insufficient” refers to the fact that each speaker utterances in the data set does not contains all the variability conditions of interest.

  3. http://www.nist.gov/itl/.

  4. The UBM refers to a universal background model of the population.

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Authors and Affiliations

  1. Advanced Technologies Application Center (CENATAV), 7a.A # 21406 e/ 214 y 216, Playa, CP 12200, Havana, Cuba

    Flavio J. Reyes-Díaz, Gabriel Hernández-Sierra & José R. Calvo de Lara

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  1. Flavio J. Reyes-Díaz
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  2. Gabriel Hernández-Sierra
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  3. José R. Calvo de Lara
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Correspondence to Flavio J. Reyes-Díaz.

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Reyes-Díaz, F.J., Hernández-Sierra, G. & Calvo de Lara, J.R. Two-space variability compensation technique for speaker verification in short length and reverberant environments. Int J Speech Technol 20, 475–485 (2017). https://doi.org/10.1007/s10772-017-9414-4

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