Acoustic model optimization for automatic pronunciation quality assessment

Golden acoustic models, which are built or optimized using standard pronunciation data, are widely used in automatic pronunciation quality assessment. However, this work points out that because of the mismatch between training and test, golden acoustic models are unable to accurately measure the pronunciation quality for accented speeches. To deal with the problem, this paper presents a novel approach which uses both standard and non-standard speeches to optimize acoustic model by minimizing the root mean square error between human and machine scores. And we also derive an EBW-like algorithm for parameter optimization. The experimental results proved the effectiveness. The cross correlation increases from 0.610 to 0.713 and the root mean square error reduces from 1.930 to 1.685.