Title :
Semi-supervised learning based Chinese dialect identification
Author :
Mingliang, Gu ; Yuguo, Xia ; Yiming, Yang
Author_Institution :
Sch. of Phys. & Electron. Eng., Xuzhou Normal Univ., Xuzhou
Abstract :
The main contribution of this paper is to present the novel information fusing framework. The shifted delta cepstra (SDC) feature and prosodic feature are firstly used to train two base classifiers respectively. Then co-training algorithm presented in semi-supervised learning is employed to improve Chinese dialect identification accuracy. Four Chinese dialects is tested with this system. The experimental results showed that the proposed system outperformed the original GMM based system.
Keywords :
Gaussian processes; feature extraction; learning (artificial intelligence); sensor fusion; speech processing; Chinese dialect identification; GMM based system; Gaussian mixture models; semisupervised learning; shifted delta cepstra feature; Artificial neural networks; Feature extraction; Hidden Markov models; Mel frequency cepstral coefficient; Natural languages; Power system modeling; Semisupervised learning; Speech analysis; Support vector machine classification; Support vector machines;
Conference_Titel :
Signal Processing, 2008. ICSP 2008. 9th International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4244-2178-7
Electronic_ISBN :
978-1-4244-2179-4
DOI :
10.1109/ICOSP.2008.4697443
