Fundamentals of automatic speech recognition

Ruhr-Universität Bochum

Docent

• Dorothea Kolossa

Type

Lecture

Term

summer semester

Subject type

Compulsory

Study path

Master Elektrotechnik & Informationstechnik; Angewandte Informatik

Specialisation/branch

Keywords

Grund­auf­ga­ben von Ad­ap­ti­ven Fil­tern: Iden­ti­fi­ka­ti­on, Fil­te­rung, Prä­dik­ti­on, In­ver­si­on, MMSE Fil­ter (Wie­ner Lö­sung), Least-Squa­res Me­tho­de, Re­kur­si­ve Al­go­rith­men, Nor­ma­li­zed Least-Me­an Squa­res (NLMS), Re­cur­si­ve Least-Squa­res (RLS), Fre­quen­cy-Do­main Ad­ap­ti­ve Fil­ter (FDAF), Kal­man Fil­ter (Zu­stands­schät­zer), Zeit­va­ri­an­te Sys­te­me, Mo­del­lie­rung im Zu­stands­raum, Bei­spiel: Akus­ti­scher Zu­stands­raum, Bei­spiel: Akus­ti­scher Zu­stands­schät­zer, Di­rek­te An­wen­dung: Frei­sprech­sys­te­me, Blin­de Sys­te­mi­den­ti­fi­ka­ti­on, Mi­ni­mum-Ma­xi­mum-Ei­gen­va­lue An­satz (PCA), Sys­te­mi­den­ti­fi­ka­ti­ons­be­din­gun­gen, Sys­tem­ab­stands­ma­ße, Di­rek­te An­wen­dung: Mi­kro­fonar­rays, Nicht­li­nea­re Sys­te­me, De­fi­ni­ti­on/Be­mes­sung von Nicht­li­nea­ri­tät,Quan­ti­sie­rung, Ro­bus­ter li­nea­rer Al­go­rith­mus,Nicht­li­nea­rer Laut­spre­cher, Nicht­li­nea­re Mo­del­lie­rung/Iden­ti­fi­ka­ti­on

Total ECTS credit

6.0

Examination

MÜND­LICH 30min

Language

Webpage

http://www.ei.rub.de/studium/lehrveranstaltungen/581/

Last modified

Nov. 30, 2024, 1:43 p.m.

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