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ICASSP
2011
IEEE

Increasing discriminative capability on MAP-based mapping function estimation for acoustic model adaptation

8 years 10 months ago
Increasing discriminative capability on MAP-based mapping function estimation for acoustic model adaptation
In this study, we propose increasing discriminative power on the maximum a posteriori (MAP)-based mapping function estimation for acoustic model adaptation. Based on the effective and stable learning advantages of MAP-based estimation, we incorporate a discriminative term and derive a new objective function. By applying the new function for online mapping function estimation, we developed discriminative maximum a posteriori (DMAP) linear regression (DMAPLR) and DMAP-based ensemble speaker and speaking environment modeling (DMAP-based ESSEM). We evaluate the DMAPLR and DMAP-based ESSEM on the Aurora-2 task in a supervised adaptation mode. The experimental results show that both DMAPLR and DMAP-based ESSEM consistently provide improvements over their ML-based and MAP-based counterparts irrespective of using one, two, or three adaptation utterances. From the improvements, we confirm the strong effect of increasing discriminative capability on the MAP-based mapping function estimation. Mo...
Yu Tsao, Ryosuke Isotani, Hisashi Kawai, Satoshi N
Added 21 Aug 2011
Updated 21 Aug 2011
Type Journal
Year 2011
Where ICASSP
Authors Yu Tsao, Ryosuke Isotani, Hisashi Kawai, Satoshi Nakamura
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