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- W1564041845 abstract "AbstractWe apply the technique of independent component analysis to Fourier power coefficients of speech signal frames for a blind detection of basic vectors (sources). A subset of sources corresponding to the noisy influence of basic frequency is identified and its corresponding features could be eliminated. The mixing coefficients for such sources are then determined for every speech sample. We compare our features with the Mel Frequency Cepstrum Coefficient (MFCC) features, widely used today for phoneme-based speech recognition.KeywordsSpeech RecognitionIndependent Component AnalysisAutomatic Speech RecognitionSpeaker RecognitionSpeech SampleThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves." @default.
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- W1564041845 date "2006-01-01" @default.
- W1564041845 modified "2023-10-16" @default.
- W1564041845 title "ICA-Based Speech Features in the Frequency Domain" @default.
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- W1564041845 doi "https://doi.org/10.1007/11679363_76" @default.
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