Improving the Performance of Language Identification System Using Different GMM-Training Approaches
This paper proposed a feasible system for language identification (LID) and designed four different GMM-training approaches to improve the system performance by accuracy recognition rates. In our experiment, we used these model-training approaches to evaluation on the system performance, which utilizes Linear Prediction Cepstrum Coefficients (LPCC) and Gaussian Mixture Model (GMM), rely on a 10-language task. From all the results, we found an optimal approach for training GMM in LID system, which achieves high accuracy of 85.25%, and indicated that different GMM-training approaches have different performances for LID system, but an advisable training method that proposed in our paper can greatly improve the system performance.
Donald C. Wunsch II, Honghua Tan, Dehuai Zeng, Qi Luo
W. Li et al., "Improving the Performance of Language Identification System Using Different GMM-Training Approaches", Advanced Materials Research, Vols. 121-122, pp. 496-501, 2010