Acoustic template-matching for automatic emergency state detection: An ELM based algorithm

Neurocomputing

Volumn 149, Issue Part A, 2015, Pages 426-434

  Principi, Emanuele ^a   Squartini, Stefano ^a   Cambria, Erik ^b   Piazza, Francesco ^a  

^a UNIVERSITÀ POLITECNICA DELLE MARCHE   (Italy)

^b NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

Author keywords

Automatic speech recognition; Dynamic time warping; Extreme Learning Machine; Support Vector Machine; Template matching

Indexed keywords

FEEDFORWARD NEURAL NETWORKS; KNOWLEDGE ACQUISITION; SUPPORT VECTOR MACHINES; TEMPLATE MATCHING; VECTORS;

AUTOMATIC CLASSIFICATION; AUTOMATIC SPEECH RECOGNITION; CEPSTRAL COEFFICIENTS; DYNAMIC TIME WARPING; EXTREME LEARNING MACHINE; ISOLATED SPEECH RECOGNITION; LENGTH NORMALIZATION; RECOGNITION ACCURACY;

SPEECH RECOGNITION;

ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; AUTOMATIC SPEECH RECOGNITION; CONTROLLED STUDY; DYNAMIC TIME WARPING; EXTREME LEARNING MACHINE; FEMALE; HUMAN; INFORMATION PROCESSING; MACHINE LEARNING; MALE; MATHEMATICAL PARAMETERS; MEL FREQUENCY CEPSTRAL COEFFICIENTS; POWER NORMALIZED CEPSTRAL COEFFICIENTS; RADIAL BASED FUNCTION; REAL TIME FACTOR; SINGLE HIDDEN LAYER FEEDFORWARD NEURAL NETWORK; SUPPORT VECTOR MACHINE;

EID: 84922032003     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2014.01.067     Document Type: Article

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