Bloomfield Model Based Signal Process for Networks

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.