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Acoustic Variability of Voice Signal as Factor of Information Security for Automatic Speech Recognition Systems with Tuning to User Voice

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Abstract

The phenomenon of the voice signal acoustic variability in automatic speech recognition systems is considered. There are two varieties—intra- and inter-speaker speech variability. The probabilistic cluster model of minimal speech units in the Kullback–Leibler information metric is used for their mathematical description and comparison in magnitude. On its basis, theoretical estimates of the voice signal acoustic variability for each of its varieties are obtained separately. The effect of information security in systems with tuning to the authorized user voice is described and quantitatively characterized. The intra-speaker variability is negligible in comparison with the inter-speaker variability of speech, and therefore does not have a noticeable harmful effect on the effectiveness of automatic speech recognition. The computational experiment is set up to confirm and develop the theoretical research results, where two speech streams from two different speakers are considered. The author’s software is used for its implementation. According to the experimental results we find that the level of inter-speaker speech variability in a number of cases goes beyond the inter-phonemic differences within a homogeneous speech flow. Therefore, in systems with tuning to the speaker voice, the effect of voice signal acoustic variability is not only unambiguously generally positive, namely: it is an information protection from unauthorized access, but also it is significant in terms of probability-theoretic relation. The obtained results are intended for the development of new and modernization of existing systems for automatic speech recognition, designed to work in a standalone mode.

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Notes

  1. Here we mean the obvious circumstance that the reference signals x k,r in general case can have a dimension that is much larger than the dimension of the speech frame x [12], but in this case it is not essential.

  2. The program website: https://sites.google.com/site/frompldcreators/produkty-1/phonemetraining .

  3. Sound file “5. Sounds and letters” on the site [in Russian] https://mixmuz.ru/mp3/%D0%B3%D0%BB%D0%B0%D1%81%D0%BD%D1%8B%D0%B5%20%D0%B7%D0%B2%D1%83%D0%BA%D0%B8 .

  4. Recommendations site P.1120: https://www.itu.int/ITU-T/recommendations/rec.aspx?id=13176 .

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  1. Nizhny Novgorod State Linguistic University, Nizhny Novgorod, Russia

    V. V. Savchenko

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V. V. Savchenko

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The initial version of this paper in Russian is published in the journal “Izvestiya Vysshikh Uchebnykh Zavedenii. Radioelektronika,” ISSN 2307-6011 (Online), ISSN 0021-3470 (Print) on the link http://radio.kpi.ua/article/view/S0021347020100039 with DOI: https://doi.org/10.20535/S0021347020100039

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Savchenko, V.V. Acoustic Variability of Voice Signal as Factor of Information Security for Automatic Speech Recognition Systems with Tuning to User Voice. Radioelectron.Commun.Syst. 63, 532–542 (2020). https://doi.org/10.3103/S0735272720100039

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