A Savitzky-Golay Filtering Perspective of Dynamic Feature Computation
Data(s) |
2013
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Resumo |
We address the classical problem of delta feature computation, and interpret the operation involved in terms of Savitzky- Golay (SG) filtering. Features such as themel-frequency cepstral coefficients (MFCCs), obtained based on short-time spectra of the speech signal, are commonly used in speech recognition tasks. In order to incorporate the dynamics of speech, auxiliary delta and delta-delta features, which are computed as temporal derivatives of the original features, are used. Typically, the delta features are computed in a smooth fashion using local least-squares (LS) polynomial fitting on each feature vector component trajectory. In the light of the original work of Savitzky and Golay, and a recent article by Schafer in IEEE Signal Processing Magazine, we interpret the dynamic feature vector computation for arbitrary derivative orders as SG filtering with a fixed impulse response. This filtering equivalence brings in significantly lower latency with no loss in accuracy, as validated by results on a TIMIT phoneme recognition task. The SG filters involved in dynamic parameter computation can be viewed as modulation filters, proposed by Hermansky. |
Formato |
application/pdf |
Identificador |
http://eprints.iisc.ernet.in/46056/1/ieee_sig_pro_let_20-3_281_2013.pdf Krishnan, Sunder Ram and Magimai-Doss, Mathew and Seelamantula, Chandra Sekhar (2013) A Savitzky-Golay Filtering Perspective of Dynamic Feature Computation. In: IEEE SIGNAL PROCESSING LETTERS, 20 (3). pp. 281-284. |
Publicador |
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
Relação |
http://dx.doi.org/10.1109/LSP.2013.2244593 http://eprints.iisc.ernet.in/46056/ |
Palavras-Chave | #Electrical Engineering |
Tipo |
Journal Article PeerReviewed |