13 research outputs found
ΠΠ½Π°Π»ΠΈΡΠΈΡΠ΅ΡΠΊΠΈΠΉ ΠΎΠ±Π·ΠΎΡ Π°ΡΠ΄ΠΈΠΎΠ²ΠΈΠ·ΡΠ°Π»ΡΠ½ΡΡ ΡΠΈΡΡΠ΅ΠΌ Π΄Π»Ρ ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΡ ΡΡΠ΅Π΄ΡΡΠ² ΠΈΠ½Π΄ΠΈΠ²ΠΈΠ΄ΡΠ°Π»ΡΠ½ΠΎΠΉ Π·Π°ΡΠΈΡΡ Π½Π° Π»ΠΈΡΠ΅ ΡΠ΅Π»ΠΎΠ²Π΅ΠΊΠ°
ΠΠ°ΡΠΈΠ½Π°Ρ Ρ 2019 Π³ΠΎΠ΄Π° Π²ΡΠ΅ ΡΡΡΠ°Π½Ρ ΠΌΠΈΡΠ° ΡΡΠΎΠ»ΠΊΠ½ΡΠ»ΠΈΡΡ ΡΠΎ ΡΡΡΠ΅ΠΌΠΈΡΠ΅Π»ΡΠ½ΡΠΌ ΡΠ°ΡΠΏΡΠΎΡΡΡΠ°Π½Π΅Π½ΠΈΠ΅ΠΌ ΠΏΠ°Π½Π΄Π΅ΠΌΠΈΠΈ, Π²ΡΠ·Π²Π°Π½Π½ΠΎΠΉ ΠΊΠΎΡΠΎΠ½Π°Π²ΠΈΡΡΡΠ½ΠΎΠΉ ΠΈΠ½ΡΠ΅ΠΊΡΠΈΠ΅ΠΉ COVID-19, Π±ΠΎΡΡΠ±Π° Ρ ΠΊΠΎΡΠΎΡΠΎΠΉ ΠΏΡΠΎΠ΄ΠΎΠ»ΠΆΠ°Π΅ΡΡΡ ΠΌΠΈΡΠΎΠ²ΡΠΌ ΡΠΎΠΎΠ±ΡΠ΅ΡΡΠ²ΠΎΠΌ ΠΈ ΠΏΠΎ Π½Π°ΡΡΠΎΡΡΠ΅Π΅ Π²ΡΠ΅ΠΌΡ. ΠΠ΅ΡΠΌΠΎΡΡΡ Π½Π° ΠΎΡΠ΅Π²ΠΈΠ΄Π½ΡΡ ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΡΡΡ ΡΡΠ΅Π΄ΡΡΠ² ΠΈΠ½Π΄ΠΈΠ²ΠΈΠ΄ΡΠ°Π»ΡΠ½ΠΎΠΉ Π·Π°ΡΠΈΡΡ ΠΎΡΠ³Π°Π½ΠΎΠ² Π΄ΡΡ
Π°Π½ΠΈΡ ΠΎΡ Π·Π°ΡΠ°ΠΆΠ΅Π½ΠΈΡ ΠΊΠΎΡΠΎΠ½Π°Π²ΠΈΡΡΡΠ½ΠΎΠΉ ΠΈΠ½ΡΠ΅ΠΊΡΠΈΠ΅ΠΉ, ΠΌΠ½ΠΎΠ³ΠΈΠ΅ Π»ΡΠ΄ΠΈ ΠΏΡΠ΅Π½Π΅Π±ΡΠ΅Π³Π°ΡΡ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ Π·Π°ΡΠΈΡΠ½ΡΡ
ΠΌΠ°ΡΠΎΠΊ Π΄Π»Ρ Π»ΠΈΡΠ° Π² ΠΎΠ±ΡΠ΅ΡΡΠ²Π΅Π½Π½ΡΡ
ΠΌΠ΅ΡΡΠ°Ρ
. ΠΠΎΡΡΠΎΠΌΡ Π΄Π»Ρ ΠΊΠΎΠ½ΡΡΠΎΠ»Ρ ΠΈ ΡΠ²ΠΎΠ΅Π²ΡΠ΅ΠΌΠ΅Π½Π½ΠΎΠ³ΠΎ Π²ΡΡΠ²Π»Π΅Π½ΠΈΡ Π½Π°ΡΡΡΠΈΡΠ΅Π»Π΅ΠΉ ΠΎΠ±ΡΠ΅ΡΡΠ²Π΅Π½Π½ΡΡ
ΠΏΡΠ°Π²ΠΈΠ» Π·Π΄ΡΠ°Π²ΠΎΠΎΡ
ΡΠ°Π½Π΅Π½ΠΈΡ Π½Π΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΠΎ ΠΏΡΠΈΠΌΠ΅Π½ΡΡΡ ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΡΠ΅ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΎΠ½Π½ΡΠ΅ ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΈ, ΠΊΠΎΡΠΎΡΡΠ΅ Π±ΡΠ΄ΡΡ Π΄Π΅ΡΠ΅ΠΊΡΠΈΡΠΎΠ²Π°ΡΡ Π·Π°ΡΠΈΡΠ½ΡΠ΅ ΠΌΠ°ΡΠΊΠΈ Π½Π° Π»ΠΈΡΠ°Ρ
Π»ΡΠ΄Π΅ΠΉ ΠΏΠΎ Π²ΠΈΠ΄Π΅ΠΎ- ΠΈ Π°ΡΠ΄ΠΈΠΎΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ. Π ΡΡΠ°ΡΡΠ΅ ΠΏΡΠΈΠ²Π΅Π΄Π΅Π½ Π°Π½Π°Π»ΠΈΡΠΈΡΠ΅ΡΠΊΠΈΠΉ ΠΎΠ±Π·ΠΎΡ ΡΡΡΠ΅ΡΡΠ²ΡΡΡΠΈΡ
ΠΈ ΡΠ°Π·ΡΠ°Π±Π°ΡΡΠ²Π°Π΅ΠΌΡΡ
ΠΈΠ½ΡΠ΅Π»Π»Π΅ΠΊΡΡΠ°Π»ΡΠ½ΡΡ
ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΎΠ½Π½ΡΡ
ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΉ Π±ΠΈΠΌΠΎΠ΄Π°Π»ΡΠ½ΠΎΠ³ΠΎ Π°Π½Π°Π»ΠΈΠ·Π° Π³ΠΎΠ»ΠΎΡΠΎΠ²ΡΡ
ΠΈ Π»ΠΈΡΠ΅Π²ΡΡ
Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊ ΡΠ΅Π»ΠΎΠ²Π΅ΠΊΠ° Π² ΠΌΠ°ΡΠΊΠ΅. Π‘ΡΡΠ΅ΡΡΠ²ΡΠ΅Ρ ΠΌΠ½ΠΎΠ³ΠΎ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠΉ Π½Π° ΡΠ΅ΠΌΡ ΠΎΠ±Π½Π°ΡΡΠΆΠ΅Π½ΠΈΡ ΠΌΠ°ΡΠΎΠΊ ΠΏΠΎ Π²ΠΈΠ΄Π΅ΠΎΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡΠΌ, ΡΠ°ΠΊΠΆΠ΅ Π² ΠΎΡΠΊΡΡΡΠΎΠΌ Π΄ΠΎΡΡΡΠΏΠ΅ ΠΌΠΎΠΆΠ½ΠΎ Π½Π°ΠΉΡΠΈ Π·Π½Π°ΡΠΈΡΠ΅Π»ΡΠ½ΠΎΠ΅ ΠΊΠΎΠ»ΠΈΡΠ΅ΡΡΠ²ΠΎ ΠΊΠΎΡΠΏΡΡΠΎΠ², ΡΠΎΠ΄Π΅ΡΠΆΠ°ΡΠΈΡ
ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡ Π»ΠΈΡ ΠΊΠ°ΠΊ Π±Π΅Π· ΠΌΠ°ΡΠΎΠΊ, ΡΠ°ΠΊ ΠΈ Π² ΠΌΠ°ΡΠΊΠ°Ρ
, ΠΏΠΎΠ»ΡΡΠ΅Π½Π½ΡΡ
ΡΠ°Π·Π»ΠΈΡΠ½ΡΠΌΠΈ ΡΠΏΠΎΡΠΎΠ±Π°ΠΌΠΈ. ΠΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠΉ ΠΈ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΎΠΊ, Π½Π°ΠΏΡΠ°Π²Π»Π΅Π½Π½ΡΡ
Π½Π° Π΄Π΅ΡΠ΅ΠΊΡΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ ΡΡΠ΅Π΄ΡΡΠ² ΠΈΠ½Π΄ΠΈΠ²ΠΈΠ΄ΡΠ°Π»ΡΠ½ΠΎΠΉ Π·Π°ΡΠΈΡΡ ΠΎΡΠ³Π°Π½ΠΎΠ² Π΄ΡΡ
Π°Π½ΠΈΡ ΠΏΠΎ Π°ΠΊΡΡΡΠΈΡΠ΅ΡΠΊΠΈΠΌ Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊΠ°ΠΌ ΡΠ΅ΡΠΈ ΡΠ΅Π»ΠΎΠ²Π΅ΠΊΠ° ΠΏΠΎΠΊΠ° Π΄ΠΎΡΡΠ°ΡΠΎΡΠ½ΠΎ ΠΌΠ°Π»ΠΎ, ΡΠ°ΠΊ ΠΊΠ°ΠΊ ΡΡΠΎ Π½Π°ΠΏΡΠ°Π²Π»Π΅Π½ΠΈΠ΅ Π½Π°ΡΠ°Π»ΠΎ ΡΠ°Π·Π²ΠΈΠ²Π°ΡΡΡΡ ΡΠΎΠ»ΡΠΊΠΎ Π² ΠΏΠ΅ΡΠΈΠΎΠ΄ ΠΏΠ°Π½Π΄Π΅ΠΌΠΈΠΈ, Π²ΡΠ·Π²Π°Π½Π½ΠΎΠΉ ΠΊΠΎΡΠΎΠ½Π°Π²ΠΈΡΡΡΠ½ΠΎΠΉ ΠΈΠ½ΡΠ΅ΠΊΡΠΈΠ΅ΠΉ COVID-19. Π‘ΡΡΠ΅ΡΡΠ²ΡΡΡΠΈΠ΅ ΡΠΈΡΡΠ΅ΠΌΡ ΠΏΠΎΠ·Π²ΠΎΠ»ΡΡΡ ΠΏΡΠ΅Π΄ΠΎΡΠ²ΡΠ°ΡΠΈΡΡ ΡΠ°ΡΠΏΡΠΎΡΡΡΠ°Π½Π΅Π½ΠΈΠ΅ ΠΊΠΎΡΠΎΠ½Π°Π²ΠΈΡΡΡΠ½ΠΎΠΉ ΠΈΠ½ΡΠ΅ΠΊΡΠΈΠΈ Ρ ΠΏΠΎΠΌΠΎΡΡΡ ΡΠ°ΡΠΏΠΎΠ·Π½Π°Π²Π°Π½ΠΈΡ Π½Π°Π»ΠΈΡΠΈΡ/ΠΎΡΡΡΡΡΡΠ²ΠΈΡ ΠΌΠ°ΡΠΎΠΊ Π½Π° Π»ΠΈΡΠ΅, ΡΠ°ΠΊΠΆΠ΅ Π΄Π°Π½Π½ΡΠ΅ ΡΠΈΡΡΠ΅ΠΌΡ ΠΏΠΎΠΌΠΎΠ³Π°ΡΡ Π² Π΄ΠΈΡΡΠ°Π½ΡΠΈΠΎΠ½Π½ΠΎΠΌ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΡΠΎΠ²Π°Π½ΠΈΠΈ COVID-19 Ρ ΠΏΠΎΠΌΠΎΡΡΡ ΠΎΠ±Π½Π°ΡΡΠΆΠ΅Π½ΠΈΡ ΠΏΠ΅ΡΠ²ΡΡ
ΡΠΈΠΌΠΏΡΠΎΠΌΠΎΠ² Π²ΠΈΡΡΡΠ½ΠΎΠΉ ΠΈΠ½ΡΠ΅ΠΊΡΠΈΠΈ ΠΏΠΎ Π°ΠΊΡΡΡΠΈΡΠ΅ΡΠΊΠΈΠΌ Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊΠ°ΠΌ. ΠΠ΄Π½Π°ΠΊΠΎ, Π½Π° ΡΠ΅Π³ΠΎΠ΄Π½ΡΡΠ½ΠΈΠΉ Π΄Π΅Π½Ρ ΡΡΡΠ΅ΡΡΠ²ΡΠ΅Ρ ΡΡΠ΄ Π½Π΅ΡΠ΅ΡΠ΅Π½Π½ΡΡ
ΠΏΡΠΎΠ±Π»Π΅ΠΌ Π² ΠΎΠ±Π»Π°ΡΡΠΈ Π°Π²ΡΠΎΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΡΠΈΠΌΠΏΡΠΎΠΌΠΎΠ² COVID-19 ΠΈ Π½Π°Π»ΠΈΡΠΈΡ/ΠΎΡΡΡΡΡΡΠ²ΠΈΡ ΠΌΠ°ΡΠΎΠΊ Π½Π° Π»ΠΈΡΠ°Ρ
Π»ΡΠ΄Π΅ΠΉ. Π ΠΏΠ΅ΡΠ²ΡΡ ΠΎΡΠ΅ΡΠ΅Π΄Ρ ΡΡΠΎ Π½ΠΈΠ·ΠΊΠ°Ρ ΡΠΎΡΠ½ΠΎΡΡΡ ΠΎΠ±Π½Π°ΡΡΠΆΠ΅Π½ΠΈΡ ΠΌΠ°ΡΠΎΠΊ ΠΈ ΠΊΠΎΡΠΎΠ½Π°Π²ΠΈΡΡΡΠ½ΠΎΠΉ ΠΈΠ½ΡΠ΅ΠΊΡΠΈΠΈ, ΡΡΠΎ Π½Π΅ ΠΏΠΎΠ·Π²ΠΎΠ»ΡΠ΅Ρ ΠΎΡΡΡΠ΅ΡΡΠ²Π»ΡΡΡ Π°Π²ΡΠΎΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΡΡ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΡ Π±Π΅Π· ΠΏΡΠΈΡΡΡΡΡΠ²ΠΈΡ ΡΠΊΡΠΏΠ΅ΡΡΠΎΠ² (ΠΌΠ΅Π΄ΠΈΡΠΈΠ½ΡΠΊΠΎΠ³ΠΎ ΠΏΠ΅ΡΡΠΎΠ½Π°Π»Π°). ΠΠ½ΠΎΠ³ΠΈΠ΅ ΡΠΈΡΡΠ΅ΠΌΡ Π½Π΅ ΡΠΏΠΎΡΠΎΠ±Π½Ρ ΡΠ°Π±ΠΎΡΠ°ΡΡ Π² ΡΠ΅ΠΆΠΈΠΌΠ΅ ΡΠ΅Π°Π»ΡΠ½ΠΎΠ³ΠΎ Π²ΡΠ΅ΠΌΠ΅Π½ΠΈ, ΠΈΠ·-Π·Π° ΡΠ΅Π³ΠΎ Π½Π΅Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎ ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄ΠΈΡΡ ΠΊΠΎΠ½ΡΡΠΎΠ»Ρ ΠΈ ΠΌΠΎΠ½ΠΈΡΠΎΡΠΈΠ½Π³ Π½ΠΎΡΠ΅Π½ΠΈΡ Π·Π°ΡΠΈΡΠ½ΡΡ
ΠΌΠ°ΡΠΎΠΊ Π² ΠΎΠ±ΡΠ΅ΡΡΠ²Π΅Π½Π½ΡΡ
ΠΌΠ΅ΡΡΠ°Ρ
. Π’Π°ΠΊΠΆΠ΅ Π±ΠΎΠ»ΡΡΠΈΠ½ΡΡΠ²ΠΎ ΡΡΡΠ΅ΡΡΠ²ΡΡΡΠΈΡ
ΡΠΈΡΡΠ΅ΠΌ Π½Π΅Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎ Π²ΡΡΡΠΎΠΈΡΡ Π² ΡΠΌΠ°ΡΡΡΠΎΠ½, ΡΡΠΎΠ±Ρ ΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΠ΅Π»ΠΈ ΠΌΠΎΠ³Π»ΠΈ Π² Π»ΡΠ±ΠΎΠΌ ΠΌΠ΅ΡΡΠ΅ ΠΏΡΠΎΠΈΠ·Π²Π΅ΡΡΠΈ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ Π½Π°Π»ΠΈΡΠΈΡ ΠΊΠΎΡΠΎΠ½Π°Π²ΠΈΡΡΡΠ½ΠΎΠΉ ΠΈΠ½ΡΠ΅ΠΊΡΠΈΠΈ. ΠΡΠ΅ ΠΎΠ΄Π½ΠΎΠΉ ΠΎΡΠ½ΠΎΠ²Π½ΠΎΠΉ ΠΏΡΠΎΠ±Π»Π΅ΠΌΠΎΠΉ ΡΠ²Π»ΡΠ΅ΡΡΡ ΡΠ±ΠΎΡ Π΄Π°Π½Π½ΡΡ
ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ², Π·Π°ΡΠ°ΠΆΠ΅Π½Π½ΡΡ
COVID-19, ΡΠ°ΠΊ ΠΊΠ°ΠΊ ΠΌΠ½ΠΎΠ³ΠΈΠ΅ Π»ΡΠ΄ΠΈ Π½Π΅ ΡΠΎΠ³Π»Π°ΡΠ½Ρ ΡΠ°ΡΠΏΡΠΎΡΡΡΠ°Π½ΡΡΡ ΠΊΠΎΠ½ΡΠΈΠ΄Π΅Π½ΡΠΈΠ°Π»ΡΠ½ΡΡ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΡ
ΠΠ½Π°Π»ΠΈΠ· ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΎΠ½Π½ΠΎΠ³ΠΎ ΠΈ ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΎΠ±Π΅ΡΠΏΠ΅ΡΠ΅Π½ΠΈΡ Π΄Π»Ρ ΡΠ°ΡΠΏΠΎΠ·Π½Π°Π²Π°Π½ΠΈΡ Π°ΡΡΠ΅ΠΊΡΠΈΠ²Π½ΡΡ ΡΠΎΡΡΠΎΡΠ½ΠΈΠΉ ΡΠ΅Π»ΠΎΠ²Π΅ΠΊΠ°
Π ΡΡΠ°ΡΡΠ΅ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½ Π°Π½Π°Π»ΠΈΡΠΈΡΠ΅ΡΠΊΠΈΠΉ ΠΎΠ±Π·ΠΎΡ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠΉ Π² ΠΎΠ±Π»Π°ΡΡΠΈ Π°ΡΡΠ΅ΠΊΡΠΈΠ²Π½ΡΡ
Π²ΡΡΠΈΡΠ»Π΅Π½ΠΈΠΉ. ΠΡΠΎ Π½Π°ΠΏΡΠ°Π²Π»Π΅Π½ΠΈΠ΅ ΡΠ²Π»ΡΠ΅ΡΡΡ ΡΠΎΡΡΠ°Π²Π»ΡΡΡΠ΅ΠΉ ΠΈΡΠΊΡΡΡΡΠ²Π΅Π½Π½ΠΎΠ³ΠΎ ΠΈΠ½ΡΠ΅Π»Π»Π΅ΠΊΡΠ°, ΠΈ ΠΈΠ·ΡΡΠ°Π΅Ρ ΠΌΠ΅ΡΠΎΠ΄Ρ, Π°Π»Π³ΠΎΡΠΈΡΠΌΡ ΠΈ ΡΠΈΡΡΠ΅ΠΌΡ Π΄Π»Ρ Π°Π½Π°Π»ΠΈΠ·Π° Π°ΡΡΠ΅ΠΊΡΠΈΠ²Π½ΡΡ
ΡΠΎΡΡΠΎΡΠ½ΠΈΠΉ ΡΠ΅Π»ΠΎΠ²Π΅ΠΊΠ° ΠΏΡΠΈ Π΅Π³ΠΎ Π²Π·Π°ΠΈΠΌΠΎΠ΄Π΅ΠΉΡΡΠ²ΠΈΠΈ Ρ Π΄ΡΡΠ³ΠΈΠΌΠΈ Π»ΡΠ΄ΡΠΌΠΈ, ΠΊΠΎΠΌΠΏΡΡΡΠ΅ΡΠ½ΡΠΌΠΈ ΡΠΈΡΡΠ΅ΠΌΠ°ΠΌΠΈ ΠΈΠ»ΠΈ ΡΠΎΠ±ΠΎΡΠ°ΠΌΠΈ. Π ΠΎΠ±Π»Π°ΡΡΠΈ ΠΈΠ½ΡΠ΅Π»Π»Π΅ΠΊΡΡΠ°Π»ΡΠ½ΠΎΠ³ΠΎ Π°Π½Π°Π»ΠΈΠ·Π° Π΄Π°Π½Π½ΡΡ
ΠΏΠΎΠ΄ Π°ΡΡΠ΅ΠΊΡΠΎΠΌ ΠΏΠΎΠ΄ΡΠ°Π·ΡΠΌΠ΅Π²Π°Π΅ΡΡΡ ΠΏΡΠΎΡΠ²Π»Π΅Π½ΠΈΠ΅ ΠΏΡΠΈΡ
ΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠ΅Π°ΠΊΡΠΈΠΉ Π½Π° Π²ΠΎΠ·Π±ΡΠΆΠ΄Π°Π΅ΠΌΠΎΠ΅ ΡΠΎΠ±ΡΡΠΈΠ΅, ΠΊΠΎΡΠΎΡΠΎΠ΅ ΠΌΠΎΠΆΠ΅Ρ ΠΏΡΠΎΡΠ΅ΠΊΠ°ΡΡ ΠΊΠ°ΠΊ Π² ΠΊΡΠ°ΡΠΊΠΎΡΡΠΎΡΠ½ΠΎΠΌ, ΡΠ°ΠΊ ΠΈ Π² Π΄ΠΎΠ»Π³ΠΎΡΡΠΎΡΠ½ΠΎΠΌ ΠΏΠ΅ΡΠΈΠΎΠ΄Π΅, Π° ΡΠ°ΠΊΠΆΠ΅ ΠΈΠΌΠ΅ΡΡ ΡΠ°Π·Π»ΠΈΡΠ½ΡΡ ΠΈΠ½ΡΠ΅Π½ΡΠΈΠ²Π½ΠΎΡΡΡ ΠΏΠ΅ΡΠ΅ΠΆΠΈΠ²Π°Π½ΠΈΠΉ. ΠΡΡΠ΅ΠΊΡΡ Π² ΡΠ°ΡΡΠΌΠ°ΡΡΠΈΠ²Π°Π΅ΠΌΠΎΠΉ ΠΎΠ±Π»Π°ΡΡΠΈ ΡΠ°Π·Π΄Π΅Π»Π΅Π½Ρ Π½Π° 4 Π²ΠΈΠ΄Π°: Π°ΡΡΠ΅ΠΊΡΠΈΠ²Π½ΡΠ΅ ΡΠΌΠΎΡΠΈΠΈ, Π±Π°Π·ΠΎΠ²ΡΠ΅ ΡΠΌΠΎΡΠΈΠΈ, Π½Π°ΡΡΡΠΎΠ΅Π½ΠΈΠ΅ ΠΈ Π°ΡΡΠ΅ΠΊΡΠΈΠ²Π½ΡΠ΅ ΡΠ°ΡΡΡΡΠΎΠΉΡΡΠ²Π°. ΠΡΠΎΡΠ²Π»Π΅Π½ΠΈΠ΅ Π°ΡΡΠ΅ΠΊΡΠΈΠ²Π½ΡΡ
ΡΠΎΡΡΠΎΡΠ½ΠΈΠΉ ΠΎΡΡΠ°ΠΆΠ°Π΅ΡΡΡ Π² Π²Π΅ΡΠ±Π°Π»ΡΠ½ΡΡ
Π΄Π°Π½Π½ΡΡ
ΠΈ Π½Π΅Π²Π΅ΡΠ±Π°Π»ΡΠ½ΡΡ
Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊΠ°Ρ
ΠΏΠΎΠ²Π΅Π΄Π΅Π½ΠΈΡ: Π°ΠΊΡΡΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΈ Π»ΠΈΠ½Π³Π²ΠΈΡΡΠΈΡΠ΅ΡΠΊΠΈΡ
Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊΠ°Ρ
ΡΠ΅ΡΠΈ, ΠΌΠΈΠΌΠΈΠΊΠ΅, ΠΆΠ΅ΡΡΠ°Ρ
ΠΈ ΠΏΠΎΠ·Π°Ρ
ΡΠ΅Π»ΠΎΠ²Π΅ΠΊΠ°. Π ΠΎΠ±Π·ΠΎΡΠ΅ ΠΏΡΠΈΠ²ΠΎΠ΄ΠΈΡΡΡ ΡΡΠ°Π²Π½ΠΈΡΠ΅Π»ΡΠ½ΡΠΉ Π°Π½Π°Π»ΠΈΠ· ΡΡΡΠ΅ΡΡΠ²ΡΡΡΠ΅Π³ΠΎ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΎΠ½Π½ΠΎΠ³ΠΎ ΠΎΠ±Π΅ΡΠΏΠ΅ΡΠ΅Π½ΠΈΡ Π΄Π»Ρ Π°Π²ΡΠΎΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΡΠ°ΡΠΏΠΎΠ·Π½Π°Π²Π°Π½ΠΈΡ Π°ΡΡΠ΅ΠΊΡΠΈΠ²Π½ΡΡ
ΡΠΎΡΡΠΎΡΠ½ΠΈΠΉ ΡΠ΅Π»ΠΎΠ²Π΅ΠΊΠ° Π½Π° ΠΏΡΠΈΠΌΠ΅ΡΠ΅ ΡΠΌΠΎΡΠΈΠΉ, ΡΠ΅Π½ΡΠΈΠΌΠ΅Π½ΡΠ°, Π°Π³ΡΠ΅ΡΡΠΈΠΈ ΠΈ Π΄Π΅ΠΏΡΠ΅ΡΡΠΈΠΈ. ΠΠ΅ΠΌΠ½ΠΎΠ³ΠΎΡΠΈΡΠ»Π΅Π½Π½ΡΠ΅ ΡΡΡΡΠΊΠΎΡΠ·ΡΡΠ½ΡΠ΅ Π°ΡΡΠ΅ΠΊΡΠΈΠ²Π½ΡΠ΅ Π±Π°Π·Ρ Π΄Π°Π½Π½ΡΡ
ΠΏΠΎΠΊΠ° ΡΡΡΠ΅ΡΡΠ²Π΅Π½Π½ΠΎ ΡΡΡΡΠΏΠ°ΡΡ ΠΏΠΎ ΠΎΠ±ΡΠ΅ΠΌΡ ΠΈ ΠΊΠ°ΡΠ΅ΡΡΠ²Ρ ΡΠ»Π΅ΠΊΡΡΠΎΠ½Π½ΡΠΌ ΡΠ΅ΡΡΡΡΠ°ΠΌ Π½Π° Π΄ΡΡΠ³ΠΈΡ
ΠΌΠΈΡΠΎΠ²ΡΡ
ΡΠ·ΡΠΊΠ°Ρ
, ΡΡΠΎ ΠΎΠ±ΡΡΠ»Π°Π²Π»ΠΈΠ²Π°Π΅Ρ Π½Π΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΠΎΡΡΡ ΡΠ°ΡΡΠΌΠΎΡΡΠ΅Π½ΠΈΡ ΡΠΈΡΠΎΠΊΠΎΠ³ΠΎ ΡΠΏΠ΅ΠΊΡΡΠ° Π΄ΠΎΠΏΠΎΠ»Π½ΠΈΡΠ΅Π»ΡΠ½ΡΡ
ΠΏΠΎΠ΄Ρ
ΠΎΠ΄ΠΎΠ², ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² ΠΈ Π°Π»Π³ΠΎΡΠΈΡΠΌΠΎΠ², ΠΏΡΠΈΠΌΠ΅Π½ΡΠ΅ΠΌΡΡ
Π² ΡΡΠ»ΠΎΠ²ΠΈΡΡ
ΠΎΠ³ΡΠ°Π½ΠΈΡΠ΅Π½Π½ΠΎΠ³ΠΎ ΠΎΠ±ΡΠ΅ΠΌΠ° ΠΎΠ±ΡΡΠ°ΡΡΠΈΡ
ΠΈ ΡΠ΅ΡΡΠΎΠ²ΡΡ
Π΄Π°Π½Π½ΡΡ
, ΠΈ ΡΡΠ°Π²ΠΈΡ Π·Π°Π΄Π°ΡΡ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΊΠΈ Π½ΠΎΠ²ΡΡ
ΠΏΠΎΠ΄Ρ
ΠΎΠ΄ΠΎΠ² ΠΊ Π°ΡΠ³ΠΌΠ΅Π½ΡΠ°ΡΠΈΠΈ Π΄Π°Π½Π½ΡΡ
, ΠΏΠ΅ΡΠ΅Π½ΠΎΡΡ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ ΠΈ Π°Π΄Π°ΠΏΡΠ°ΡΠΈΠΈ ΠΈΠ½ΠΎΡΠ·ΡΡΠ½ΡΡ
ΡΠ΅ΡΡΡΡΠΎΠ². Π ΡΡΠ°ΡΡΠ΅ ΠΏΡΠΈΠ²ΠΎΠ΄ΠΈΡΡΡ ΠΎΠΏΠΈΡΠ°Π½ΠΈΠ΅ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² Π°Π½Π°Π»ΠΈΠ·Π° ΠΎΠ΄Π½ΠΎΠΌΠΎΠ΄Π°Π»ΡΠ½ΠΎΠΉ Π²ΠΈΠ·ΡΠ°Π»ΡΠ½ΠΎΠΉ, Π°ΠΊΡΡΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΈ Π»ΠΈΠ½Π³Π²ΠΈΡΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ, Π° ΡΠ°ΠΊΠΆΠ΅ ΠΌΠ½ΠΎΠ³ΠΎΠΌΠΎΠ΄Π°Π»ΡΠ½ΡΡ
ΠΏΠΎΠ΄Ρ
ΠΎΠ΄ΠΎΠ² ΠΊ ΡΠ°ΡΠΏΠΎΠ·Π½Π°Π²Π°Π½ΠΈΡ Π°ΡΡΠ΅ΠΊΡΠΈΠ²Π½ΡΡ
ΡΠΎΡΡΠΎΡΠ½ΠΈΠΉ. ΠΠ½ΠΎΠ³ΠΎΠΌΠΎΠ΄Π°Π»ΡΠ½ΡΠΉ ΠΏΠΎΠ΄Ρ
ΠΎΠ΄ ΠΊ Π°Π²ΡΠΎΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠΌΡ Π°Π½Π°Π»ΠΈΠ·Ρ Π°ΡΡΠ΅ΠΊΡΠΈΠ²Π½ΡΡ
ΡΠΎΡΡΠΎΡΠ½ΠΈΠΉ ΠΏΠΎΠ·Π²ΠΎΠ»ΡΠ΅Ρ ΠΏΠΎΠ²ΡΡΠΈΡΡ ΡΠΎΡΠ½ΠΎΡΡΡ ΡΠ°ΡΠΏΠΎΠ·Π½Π°Π²Π°Π½ΠΈΡ ΡΠ°ΡΡΠΌΠ°ΡΡΠΈΠ²Π°Π΅ΠΌΡΡ
ΡΠ²Π»Π΅Π½ΠΈΠΉ ΠΎΡΠ½ΠΎΡΠΈΡΠ΅Π»ΡΠ½ΠΎ ΠΎΠ΄Π½ΠΎΠΌΠΎΠ΄Π°Π»ΡΠ½ΡΡ
ΡΠ΅ΡΠ΅Π½ΠΈΠΉ. Π ΠΎΠ±Π·ΠΎΡΠ΅ ΠΎΡΠΌΠ΅ΡΠ΅Π½Π° ΡΠ΅Π½Π΄Π΅Π½ΡΠΈΡ ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΡΡ
ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠΉ, Π·Π°ΠΊΠ»ΡΡΠ°ΡΡΠ°ΡΡΡ Π² ΡΠΎΠΌ, ΡΡΠΎ Π½Π΅ΠΉΡΠΎΡΠ΅ΡΠ΅Π²ΡΠ΅ ΠΌΠ΅ΡΠΎΠ΄Ρ ΠΏΠΎΡΡΠ΅ΠΏΠ΅Π½Π½ΠΎ Π²ΡΡΠ΅ΡΠ½ΡΡΡ ΠΊΠ»Π°ΡΡΠΈΡΠ΅ΡΠΊΠΈΠ΅ Π΄Π΅ΡΠ΅ΡΠΌΠΈΠ½ΠΈΡΠΎΠ²Π°Π½Π½ΡΠ΅ ΠΌΠ΅ΡΠΎΠ΄Ρ Π±Π»Π°Π³ΠΎΠ΄Π°ΡΡ Π»ΡΡΡΠ΅ΠΌΡ ΠΊΠ°ΡΠ΅ΡΡΠ²Ρ ΡΠ°ΡΠΏΠΎΠ·Π½Π°Π²Π°Π½ΠΈΡ ΡΠΎΡΡΠΎΡΠ½ΠΈΠΉ ΠΈ ΠΎΠΏΠ΅ΡΠ°ΡΠΈΠ²Π½ΠΎΠΉ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠ΅ Π±ΠΎΠ»ΡΡΠΎΠ³ΠΎ ΠΎΠ±ΡΠ΅ΠΌΠ° Π΄Π°Π½Π½ΡΡ
. Π ΡΡΠ°ΡΡΠ΅ ΡΠ°ΡΡΠΌΠ°ΡΡΠΈΠ²Π°ΡΡΡΡ ΠΌΠ΅ΡΠΎΠ΄Ρ Π°Π½Π°Π»ΠΈΠ·Π° Π°ΡΡΠ΅ΠΊΡΠΈΠ²Π½ΡΡ
ΡΠΎΡΡΠΎΡΠ½ΠΈΠΉ. ΠΡΠ΅ΠΈΠΌΡΡΠ΅ΡΡΠ²ΠΎΠΌ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΡ ΠΌΠ½ΠΎΠ³ΠΎΠ·Π°Π΄Π°ΡΠ½ΡΡ
ΠΈΠ΅ΡΠ°ΡΡ
ΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΏΠΎΠ΄Ρ
ΠΎΠ΄ΠΎΠ² ΡΠ²Π»ΡΠ΅ΡΡΡ Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΡΡΡ ΠΈΠ·Π²Π»Π΅ΠΊΠ°ΡΡ Π½ΠΎΠ²ΡΠ΅ ΡΠΈΠΏΡ Π·Π½Π°Π½ΠΈΠΉ, Π² ΡΠΎΠΌ ΡΠΈΡΠ»Π΅ ΠΎ Π²Π»ΠΈΡΠ½ΠΈΠΈ, ΠΊΠΎΡΡΠ΅Π»ΡΡΠΈΠΈ ΠΈ Π²Π·Π°ΠΈΠΌΠΎΠ΄Π΅ΠΉΡΡΠ²ΠΈΠΈ Π½Π΅ΡΠΊΠΎΠ»ΡΠΊΠΈΡ
Π°ΡΡΠ΅ΠΊΡΠΈΠ²Π½ΡΡ
ΡΠΎΡΡΠΎΡΠ½ΠΈΠΉ Π΄ΡΡΠ³ Π½Π° Π΄ΡΡΠ³Π°, ΡΡΠΎ ΠΏΠΎΡΠ΅Π½ΡΠΈΠ°Π»ΡΠ½ΠΎ Π²Π»Π΅ΡΠ΅Ρ ΠΊ ΡΠ»ΡΡΡΠ΅Π½ΠΈΡ ΠΊΠ°ΡΠ΅ΡΡΠ²Π° ΡΠ°ΡΠΏΠΎΠ·Π½Π°Π²Π°Π½ΠΈΡ. ΠΡΠΈΠ²ΠΎΠ΄ΡΡΡΡ ΠΏΠΎΡΠ΅Π½ΡΠΈΠ°Π»ΡΠ½ΡΠ΅ ΡΡΠ΅Π±ΠΎΠ²Π°Π½ΠΈΡ ΠΊ ΡΠ°Π·ΡΠ°Π±Π°ΡΡΠ²Π°Π΅ΠΌΡΠΌ ΡΠΈΡΡΠ΅ΠΌΠ°ΠΌ Π°Π½Π°Π»ΠΈΠ·Π° Π°ΡΡΠ΅ΠΊΡΠΈΠ²Π½ΡΡ
ΡΠΎΡΡΠΎΡΠ½ΠΈΠΉ ΠΈ ΠΎΡΠ½ΠΎΠ²Π½ΡΠ΅ Π½Π°ΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ Π΄Π°Π»ΡΠ½Π΅ΠΉΡΠΈΡ
ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠΉ
Multi-Corpus Learning for AudioβVisual Emotions and Sentiment Recognition
Recognition of emotions and sentiment (affective states) from human audioβvisual information is widely used in healthcare, education, entertainment, and other fields; therefore, it has become a highly active research area. The large variety of corpora with heterogeneous data available for the development of single-corpus approaches for recognition of affective states may lead to approaches trained on one corpus being less effective on another. In this article, we propose a multi-corpus learned audioβvisual approach for emotion and sentiment recognition. It is based on the extraction of mid-level features at the segment level using two multi-corpus temporal models (a pretrained transformer with GRU layers for the audio modality and pre-trained 3D CNN with BiLSTM-Former for the video modality) and on predicting affective states using two single-corpus cross-modal gated self-attention fusion (CMGSAF) models. The proposed approach was tested on the RAMAS and CMU-MOSEI corpora. To date, our approach has outperformed state-of-the-art audioβvisual approaches for emotion recognition by 18.2% (78.1% vs. 59.9%) for the CMU-MOSEI corpus in terms of the Weighted Accuracy and by 0.7% (82.8% vs. 82.1%) for the RAMAS corpus in terms of the Unweighted Average Recall
Combining Clustering and Functionals based Acoustic Feature Representations for Classification of Baby Sounds
This paper investigates different fusion strategies as well as provides insights on their effectiveness alongside standalone classifiers in the framework of paralinguistic analysis of infant vocalizations. The combinations of such systems as Support Vector Machines (SVM) and Extreme Learning Machines (ELM) based classifiers, as well as its weighted kernel version are explored, training systems on different acoustic feature representations and implementing weighted score-level fusion of the predictions. The proposed framework is tested on INTERSPEECH ComParE-2019 Baby Sounds corpus, which is a collection of Home Bank infant vocalization corpora annotated for five classes. Adhering to the challenge protocol, using a single test set submission we outperform the challenge baseline Unweighted Average Recall (UAR) score and achieve a comparable result to the state-of-the-art
Complex Paralinguistic Analysis of Speech: Predicting Gender, Emotions and Deception in a Hierarchical Framework
In this paper, we present a hierarchical framework for complex paralinguistic analysis of speech including gender, emotions and deception recognition. The main idea of the framework is built upon the research on interrelation between various paralinguistic phenomena. It uses gender information to predict emotional states, and the outcome of the emotion recognition to predict the truthfulness of the speech. We use multiple datasets (aGender, Ruslana, EmoDB and DSD) to perform within-corpus and cross-corpus experiments using various performance measures. The experimental results reveal that gender-specific models improve the effectiveness of automatic speech emotion recognition in terms of Unweighted Average Recall up to an absolute 5.7%, and the integration of emotion predictions improves the F-score of automatic deception detection compared to our baseline by an absolute 4.7%. The obtained cross-validation results of 88.4 +/- 1.5% for deception detection beat the existing state-of-the-art by an absolute 2.8%
Multimodal Personality Traits Assessment (MuPTA) Corpus: The Impact of Spontaneous and Read Speech
Automatic personality traits assessment (PTA) provides high-level, intelligible predictive inputs for subsequent critical downstream tasks, such as job interview recommendations and mental healthcare monitoring. In this work, we introduce a novel Multimodal Personality Traits Assessment (MuPTA) corpus. Our MuPTA corpus is unique in that it contains both spontaneous and read speech collected in the midly-resourced Russian language. We present a novel audio-visual approach for PTA that is used in order to set up baseline results on this corpus. We further analyze the impact of both spontaneous and read speech types on the PTA predictive performance. We find that for the audio modality, the PTA predictive performances on short signals are almost equal regardless of the speech type, while PTA using video modality is more accurate with spontaneous speech compared to read one regardless of the signal length
End-to-End Modeling and Transfer Learning for Audiovisual Emotion Recognition in-the-Wild
As emotions play a central role in human communication, automatic emotion recognition has attracted increasing attention in the last two decades. While multimodal systems enjoy high performances on lab-controlled data, they are still far from providing ecological validity on non-lab-controlled, namely βin-the-wildβ data. This work investigates audiovisual deep learning approaches to emotion recognition in in-the-wild problem. Inspired by the outstanding performance of end-to-end and transfer learning techniques, we explored the effectiveness of architectures in which a modality-specific Convolutional Neural Network (CNN) is followed by a Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) using the AffWild2 dataset under the Affective Behavior Analysis in-the-Wild (ABAW) challenge protocol. We deployed unimodal end-to-end and transfer learning approaches within a multimodal fusion system, which generated final predictions using a weighted score fusion scheme. Exploiting the proposed deep-learning-based multimodal system, we reached a test set challenge performance measure of 48.1% on the ABAW 2020 Facial Expressions challenge, which advances the first-runner-up performance
Ensembling End-to-End Deep Models for Computational Paralinguistics Tasks: ComParE 2020 Mask and Breathing Sub-Challenges
This paper describes deep learning approaches for the Mask and Breathing Sub-Challenges (SCs), which are addressed by the INTERSPEECH 2020 Computational Paralinguistics Challenge. Motivated by outstanding performance of state-of-the-art end-to-end (E2E) approaches, we explore and compare effectiveness of different deep Convolutional Neural Network (CNN) architectures on raw data, log Mel-spectrograms, and Mel-Frequency Cepstral Coefficients. We apply a transfer learning approach to improve modelβs efficiency and convergence speed. In the Mask SC, we conduct experiments with several pretrained CNN architectures on log-Mel spectrograms, as well as Support Vector Machines on baseline features. For the Breathing SC, we propose an ensemble deep learning system that exploits E2E learning and sequence prediction. The E2E model is based on 1D CNN operating on raw speech signals and is coupled with Long Short-Term Memory layers for sequence modeling. The second model works with log-Mel features and is based on a pretrained 2D CNN model stacked to Gated Recurrent Unit layers. To increase performance of our models in both SCs, we use ensembles of the best deep neural models obtained from N-fold cross-validation on combined challenge training and development datasets. Our results markedly outperform the challenge test set baselines in both SCs
End-to-End Modeling and Transfer Learning for Audiovisual Emotion Recognition in-the-Wild
As emotions play a central role in human communication, automatic emotion recognition has attracted increasing attention in the last two decades. While multimodal systems enjoy high performances on lab-controlled data, they are still far from providing ecological validity on non-lab-controlled, namely βin-the-wildβ data. This work investigates audiovisual deep learning approaches to emotion recognition in in-the-wild problem. Inspired by the outstanding performance of end-to-end and transfer learning techniques, we explored the effectiveness of architectures in which a modality-specific Convolutional Neural Network (CNN) is followed by a Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) using the AffWild2 dataset under the Affective Behavior Analysis in-the-Wild (ABAW) challenge protocol. We deployed unimodal end-to-end and transfer learning approaches within a multimodal fusion system, which generated final predictions using a weighted score fusion scheme. Exploiting the proposed deep-learning-based multimodal system, we reached a test set challenge performance measure of 48.1% on the ABAW 2020 Facial Expressions challenge, which advances the first-runner-up performance