Utilizing compressibility in reconstructing spectrographic data, with applications to noise robust ASR

IEEE Signal Processing Letters

Volumn 16, Issue 5, 2009, Pages 398-401

  Borgström, Bengt J ^a   Alwan, Abeer ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Compressibility; Missing features; Noise robust automatic speech recognition; Spectral reconstruction

Indexed keywords

AUTOMATIC SPEECH RECOGNITION; FEATURE ESTIMATIONS; FEATURE-BASED; FRONT ENDS; MISSING FEATURES; NOISE ROBUST AUTOMATIC SPEECH RECOGNITION; NOVEL ALGORITHMS; OPTIMIZATION PROBLEMS; PERFORMANCE APPROACHES; QUANTITATIVE ANALYSIS; RECOGNITION ACCURACIES; ROBUST ASR; SPARSE REPRESENTATIONS; SPECTRAL RECONSTRUCTION; SPECTROGRAPHIC DATUM; SPEECH DATUM;

COMPRESSIBILITY; REMELTING; RESTORATION; SPEECH ANALYSIS;

SPEECH RECOGNITION;

EID: 64249084023     PISSN: 10709908     EISSN: None     Source Type: Journal    
DOI: 10.1109/LSP.2009.2016452     Document Type: Article

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