Ozlem Kalinli, Michael L. Seltzer, and Alex Acero
In traditional methods for noise robust automatic speech recognition,
the acoustic models are typically trained using clean speech or
using multi-condition data that is processed by the same feature enhancement
algorithm expected to be used in decoding. In this paper,
we propose a noise adaptive training (NAT) algorithm that can be
applied to all training data that normalizes the environmental distortion
as part of the model training. In contrast to the feature enhancement
methods, NAT estimates the underlying “pseudo-clean” model
parameters directly without relying on point estimates of the clean
speech features as an intermediate step. The pseudo-clean model parameters
learned with NAT are later used with vector Taylor series
(VTS) model adaptation for decoding noisy utterances at test time.
Experiments performed on the Aurora 2 and Aurora 3 tasks, demonstrate
that the proposed NAT method obtain relative improvements
of 18.83% and 32.02%, respectively, over VTS model adaptation.
In Proceedings of International Conference on Acoustics, Speech, and Signal Processing
Publisher Institute of Electrical and Electronics Engineers, Inc.
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