Bloomfield Model Based Signal Process for Networks
Changhua Yao, Lei Wang, Xiaohan Yu · IEEE Access · 2018
This paper proposes a novel speech signal analysis approach based on the Bloomfield ($BF$) model, and provides a formulation of a time-domain$BF$model for speech signals with which speech signals can be reconstructed and the relevant characteristic parameters analyzed. The relationship between the parameters of the$BF$model and those of the linear prediction ($LP$) model are derived, and the speech feature sets derived via the$LP$and$BF$models are compared. A new algorithm is proposed for the recognition of isolated digit speech that utilizes a vector quantization approach and is based on the$BF$Model. The result is obtained with this$BF$approach that provides better results than those of the$LP$model when predicting speech signals. In particular, the$BF$approach has several advantages, including fewer parameters, a lower computational complexity, and accurate characterization of speakers. These advantages ensure the utility of the$BF$model in speech processing applications.