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Acoustic Model Training Based on Spatial Correlation Transformation |
Su Teng-rong; Wu Ji; Wang Zuo-ying |
Electronic Engineering Department, Tsinghua University, Beijing 100084, China |
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Abstract In order to enhance the utilization of the correlation between different acoustic units in speech recognition, a novel model training approach based on the Spatial Correlation Transformation (SCT) framework is proposed in this paper, in which the speaker-independent model parameters are re-estimated using the spatial correlation information in the training data. In this algorithm, SCT is applied to all training data, to decrease the correlation among the training data, make the model re-estimated less dependent on the training data, and then improve the performance of the model. Experiments show that the combination of SCT-based model training and SCT-based feature transformation achieves a relative reduction of 18% of average syllable error rate compared to the baseline system.
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Received: 16 March 2009
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Corresponding Authors:
Su Teng-rong
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