A Savitzky-Golay Filtering Perspective of Dynamic Feature Computation


Autoria(s): Krishnan, Sunder Ram; Magimai-Doss, Mathew; Seelamantula, Chandra Sekhar
Data(s)

2013

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