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Erschienen in:
2006 | OriginalPaper | Buchkapitel
Vector Autoregressive Model for Missing Feature Reconstruction
verfasst von : Xiong Xiao, Haizhou Li, Eng Siong Chng
Erschienen in: Chinese Spoken Language Processing
Verlag: Springer Berlin Heidelberg
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This paper proposes a Vector Autoregressive (VAR) model as a new technique for missing feature reconstruction in ASR. We model the spectral features using multiple VAR models. A VAR model predicts missing features as a linear function of a block of feature frames. We also propose two schemes for VAR training and testing. The experiments on AURORA-2 database have validated the modeling methodology and shown that the proposed schemes are especially effective for low SNR speech signals. The best setting has achieved a recognition accuracy of 88.2% at -5dB SNR on subway noise task when oracle data mask is used.