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appropriate acoustic model adaptation schemes that mitigate the mismatch between the front- and back-ends.
Various acoustic model adaptation schemes were also found effective when jointly used with front-end processing including dereverberation.
More specifically, their processing strategies seem to commonly emphasize the joint utilization of the following: front-end processing such as beamforming and dereverberation that effectively utilize multichannel acoustic diversity by linear filtering, strong acoustic models such as DNNs, and appropriate acoustic model adaptation schemes that mitigate the mismatch between the front- and back-ends.
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The method proposed in Fig. 2 may potentially be complementary to some other reverberation-robust speech recognition methods, such as applying speaker adaptation, acoustic model adaptation or preprocessing schemes (e.g. beamforming) [40].
Then, the adaptation schemes for all other models can be derived as special cases of the HGM case.
Tachioka et al. [36] employed simple but robust front-end processing for steady improvement and focused more on strong acoustic models that were combined with various advanced training and adaptation schemes.
Acoustic model adaptation.
Fuzzy adaptation schemes are devised for the on-line adaptation of the parameters of this method.
Here, we observe significantly larger drop rates with the adaptation schemes employing the MAC layer adaptation.
The employed adaptation schemes lower this number significantly.
In contrast to previous work (where standard HMM adaptation schemes are used), linear input network adaptation is investigated.
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