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

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5 Author(s)
Remes, U. ; Sch. of Sci., Adaptive Inf. Res. Centre, Aalto Univ., Espoo, Finland ; Palomaki, K.J. ; Raiko, T. ; Honkela, A.
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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.

Published in:

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