Real-life voice activity detection with LSTM Recurrent Neural Networks and an application to Hollywood movies

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

Volumn , Issue , 2013, Pages 483-487

  Eyben, Florian ^a   Weninger, Felix ^a   Squartini, Stefano ^b   Schuller, Bjorn ^a  

^a TECHNICAL UNIVERSITY OF MUNICH   (Germany)

^b UNIVERSITÀ POLITECNICA DELLE MARCHE   (Italy)

Author keywords

Long Short Term Memory; Neural Networks; Speech Detection; Voice Activity Detection

Indexed keywords

DATA-DRIVEN APPROACH; EQUAL ERROR RATE; LONG SHORT-TERM MEMORY; LONG-TERM RECORDING; REFERENCE ALGORITHM; SPEECH DETECTION; SPONTANEOUS SPEECH; VOICE ACTIVITY DETECTION;

BRAIN; MOTION PICTURES; NEURAL NETWORKS; RECURRENT NEURAL NETWORKS; SIGNAL PROCESSING; SIGNAL TO NOISE RATIO; SPEECH RECOGNITION;

SPEECH;

EID: 84890443834     PISSN: 15206149     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICASSP.2013.6637694     Document Type: Conference Paper

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