Applying multiple classifiers and non-linear dynamics features for detecting sleepiness from speech

Neurocomputing

Volumn 84, Issue , 2012, Pages 65-75

  Krajewski, Jarek ^a   Schnieder, Sebastian ^a   Sommer, David ^b   Batliner, Anton ^c   Schuller, Björn ^d  

^a UNIVERSITY OF WUPPERTAL   (Germany)

^b University of Applied Sciences Schmalkalden   (Germany)

^c UNIVERSITY OF ERLANGEN NUREMBERG   (Germany)

^d TECHNICAL UNIVERSITY OF MUNICH   (Germany)

Author keywords

Bagging; Boosting; Non linear dynamics; Sleepiness; Speech

Indexed keywords

BAGGING; BAYES NET; BEST MODEL; BOOSTING; CLASSIFICATION MODELS; EMBEDDING DIMENSIONS; ENSEMBLE CLASSIFIERS; FATIGUE DETECTION; FEATURE EXTRACTION METHODS; FEATURE SETS; FEATURE SUBSET SELECTION; MALE SPEAKERS; MULTIPLE CLASSIFIERS; NEAREST NEIGHBORS; NON-LINEAR DYNAMICS; RECONSTRUCTED PHASE SPACE; REFERENCE VALUES; SLEEP DEPRIVATION; SLEEPINESS; SPEAKER CLASSIFICATION; VOWEL PHONATIONS;

ADAPTIVE BOOSTING; DYNAMICS; FEATURE EXTRACTION; LINGUISTICS; OBSERVABILITY; PHASE SPACE METHODS; SPEECH; SPEECH PROCESSING; STATISTICAL METHODS;

SPEECH RECOGNITION;

ARTICLE; AUDITORY DISCRIMINATION; CLASSIFIER; CONTROLLED STUDY; CORRELATION ANALYSIS; FEMALE; HUMAN; HUMAN EXPERIMENT; K NEAREST NEIGHBOR; KAROLINSKA SLEEPINESS SCALE; MACHINE LEARNING; MALE; NONLINEAR SYSTEM; NORMAL HUMAN; PHONATION; PHONETICS; PRIORITY JOURNAL; RATING SCALE; REFERENCE VALUE; SELF REPORT; SEX DIFFERENCE; SLEEP DEPRIVATION; SOMNOLENCE; SPEECH ANALYSIS; VOWEL;

EID: 84858159716     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2011.12.021     Document Type: Article

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