Denoising and recognition using hidden Markov models with observation distributions modeled by hidden Markov trees

Pattern Recognition

Volumn 43, Issue 4, 2010, Pages 1577-1589

  Milone, Diego H ^a   Di Persia, Leandro E ^a   Torres, María E ^a,b  

^a UNIVERSIDAD NACIONAL DEL LITORAL   (Argentina)

^b UNIVERSIDAD NACIONAL DE ENTRE RÍOS   (Argentina)

Author keywords

EM algorithm; Sequence learning; Speech recognition; Wavelets

Indexed keywords

ADDITIVE WHITE NOISE; COMPOSITE MODELS; DE-NOISING; DOPPLER; EM ALGORITHMS; EXPECTATION-MAXIMIZATION FRAMEWORKS; GAUSSIAN MIXTURES; HIDDEN MARKOV TREE; INTERNAL STATE; LEARNING ARCHITECTURES; LEARNING MODELS; LOCAL DYNAMICS; LONG-TERM DEPENDENCIES; MACHINE-LEARNING; MEAN SQUARED ERROR; MODEL VARIABLES; MODEL-BASED; NEW TOOLS; PHONEME RECOGNITION; REAL APPLICATIONS; SEQUENCE LEARNING; STATIONARITY; TIME SEQUENCES; TRAINING ALGORITHMS; WAVELET COEFFICIENTS; WAVELET DOMAIN;

CONTROL SYSTEM ANALYSIS; FEATURE EXTRACTION; IMAGE ANALYSIS; IMAGE SEGMENTATION; LEARNING ALGORITHMS; OBJECT RECOGNITION; REAL VARIABLES; SPEECH RECOGNITION; WAVELET TRANSFORMS;

HIDDEN MARKOV MODELS;

EID: 74449091787     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2009.11.010     Document Type: Article

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