10th Annual Conference of the International Speech Communication Association

Brighton, United Kingdom
September 6-10, 2009

Dimension Reducing of LSF parameters Based on Radial Basis Function Neural Network

Hongjun Sun, Jianhua Tao, Huibin Jia

Chinese Academy of Sciences, China

In this paper, we investigate a novel method for transforming line spectral frequency (LSF) parameters to lower dimensional coefficients. Radial basis function neutral network (RBF NN) based transforming model is used to fit LSF vectors. In the training process, two criterions, including mean squared error and weighted mean squared error, are involved to measure distance between original vector and approximate vector. Besides, features of LSF parameters are taken into account to supervise the training process. As a result, LSF vectors are represented by the coefficient vectors of transforming model. The experimental results reveal that 24-order LSF vector can be transformed to 15-dimension coefficient vector with an average spectral distortion of approximately 1dB. Subjective evaluation manifests that the transforming method in this paper will not lead to significant voice quality decreasing.

Full Paper

Bibliographic reference.  Sun, Hongjun / Tao, Jianhua / Jia, Huibin (2009): "Dimension reducing of LSF parameters based on radial basis function neural network", In INTERSPEECH-2009, 1103-1106.