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Missing-Feature Reconstruction With a Bounded Nonlinear State-Space Model

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5 Author(s)
Ulpu Remes ; Adaptive Informatics Research Centre, Aalto University School of Science, Finland ; Kalle J. Palomaki ; Tapani Raiko ; Antti Honkela
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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.

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IEEE Signal Processing Letters  (Volume:18 ,  Issue: 10 )