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Generalized sub band analysis and signal synthesis
Zhilyakov, E. G., Belov, S. P., Oleinik, I. I., Babarinov, S. L., Trubitsyna, D. I.
The present paper introduces the basics of building a theory forsubband analysis/signal synthesis
Адаптация методов цифровой обработки сигналов к задаче анализа речи при неврологических патологиях
Куль, Т. П., Рушкевич, Ю. Н., Лихачев, С. А., Kul, T. P., Rushkevich, Y. N., Lihachev, S. A.
диагностики бульбарного синдрома. The methodical and software for the speech signal analysis at neurological
About speech data compression
Zhilyakov, E. G., Belov, S. P., Belov, A. S., Medvedeva, A. A.
, and by the compression of speech signals generated by the actual speech sounds, which allowed to reduce the cost of ITS
Analysis of the acoustic channel of voice information leakage in a protected room
Hasani, A. N. S.
Protection of speech information in a dedicated room begins with the assessment
Religious and secular basics in preaching of Patriarch Kirill
Chernysheva A.
The article considers religious and secular speech-behavioral tactics to condemn sin (anger, envy
Синтез измерительного композитного сигнала для оценки защищенности речевых сигналов при дискретно-квантованном преобразовании
Железняк, В. К., Лавров, С. В., Филиппович, А. Г., Барановский, М. М., Zheleznjak, V. K., Lavrov, S. V., Filipovich, A. G., Baranouski, M. M.
in high-level noise by discrete-quantized representation of speech signals using the principles
Программный модуль биометрической верификации
Куницкий, Ю. О., Kunitskiy, Yu. O.
speech signals
Speech emotion recognition using SVM classifier with suprasegmental MFCC features
Krasnoproshin, D., Vashkevich, M.
This study explores speech emotion recognition (SER) using mel-frequency cepstral
Model representation of vibroacoustic channels of speech information leakage
Hasani Ahmed Nezar Salih
. It is shown that the spectrum of speech signals can overlap with a set of a number of natural resonant
Ruslan: Russian spoken language corpus for speech synthesis
Gabdrakhmanov L., Garaev R., Razinkov E.
corpus for the text-to-speech task. RUSLAN contains 22200 audio samples with text annotations – more than

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