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Class-Based Parametric Approximation to Histogram Equalization for ASR
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DOI：10.1109/LSP.2012.2199485.png)
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Abstract
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This letter assesses an improved equalization transformation for robust speech recognition in noisy environments. The proposal is an evolution of the parametric approximation to Histogram Equalization named PEQ into a two-step algorithm dealing separately with environmental and acoustic mismatch. A first parametric equalization is done to eliminate environmental mismatch. These equalized data are divided into classes, and parametrically re-equalized using class specific references to reduce the acoustic mismatch. Experiments have been conducted for Aurora 2 and Aurora 4 databases. A comparative analysis of the experimental results shows significant benefits for databases with high acoustic variability like Aurora 4.
Keywords:
Feature compensation
histogram equalization
parametric equalization
probabilistic classes
robust ASR
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