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We propose a novel approach of using Cross Validation (CV) and Speaker Clustering (SC) based data samplings to construct an ensemble of acoustic models for speech recognition. We also investigate the effects of the existing techniques of Cross Validation Expectation Maximization (CVEM), Discriminative Training (DT), and Multiple Layer Perceptron (MLP) features on the quality of the proposed ensemble...
In this paper, we first described the automatic Spoken Chinese Test (SCT). With a large amount of native and non-native data collected for SCT, different training strategies for acoustic modeling were investigated. Evaluations were performed on native as well as non-native datasets. We discovered that directly combining native and non-native data to train acoustic models did not work well, and the...
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