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A Savitzky-Golay Filtering Perspective of Dynamic Feature Computation

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.

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Official URL: http://dx.doi.org/10.1109/LSP.2013.2244593


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.

Item Type: Journal Article
Additional Information: Copyright for this article belongs to IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC,USA
Keywords: Dynamic features;modulation filtering;Savitzky-Golay (SG) filtering;speech recognition
Department/Centre: Division of Electrical Sciences > Electrical Engineering
Date Deposited: 15 Mar 2013 05:51
Last Modified: 15 Mar 2013 05:54
URI: http://eprints.iisc.ac.in/id/eprint/46056

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