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Missing-Feature Reconstruction With a Bounded Nonlinear State-Space Model
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DOI：10.1109/LSP.2011.2163508.png)
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摘要
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Missing-feature reconstruction can improve speech recognition performance in unknown noisy environments. In this work, we examine using a nonlinear state-space model (NSSM) for missing-feature reconstruction and propose estimation with observed bounds to improve the NSSM performance. Evaluated in large-vocabulary continuous speech recognition task with babble and impulsive noise, using observed bounds in NSSM state estimation significantly improved the method performance.
Keyword:
Missing data
noise robustness
speech recognition
state space methods
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