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Feature compensation based on soft decision
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DOI：10.1109/LSP.2003.821720.png)
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In this letter, we propose a novel approach to feature compensation for robust speech recognition in noisy environments. Our approach combines the interacting multiple model (IMM) and spectral subtraction (SS) techniques based on a soft decision for speech presence. The proposed approach shows 13.56% of average relative improvement compared to the IMM algorithm in the speech recognition experiments performed on the AURORA2 database when clean condition training is applied.
Keyword:
feature compensation
robust speech recognition
soft decision
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9.6
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