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This paper proposes a novel speech signal analysis approach based on the Bloomfield (<inline-formula> <tex-math notation="LaTeX">$BF$ </tex-math></inline-formula>) model, and provides a formulation of a time-domain <inline-formula> <tex-math notation="LaTeX">$BF$ </tex-math></inline-formula> model for speech signals with which speech signals can be reconstructed and the relevant characteristic parameters analyzed. The relationship between the parameters of the <inline-formula> <tex-math notation="LaTeX">$BF$ </tex-math></inline-formula> model and those of the linear prediction (<inline-formula> <tex-math notation="LaTeX">$LP$ </tex-math></inline-formula>) model are derived, and the speech feature sets derived via the <inline-formula> <tex-math notation="LaTeX">$LP$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$BF$ </tex-math></inline-formula> 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 <inline-formula> <tex-math notation="LaTeX">$BF$ </tex-math></inline-formula> Model. The result is obtained with this <inline-formula> <tex-math notation="LaTeX">$BF$ </tex-math></inline-formula> approach that provides better results than those of the <inline-formula> <tex-math notation="LaTeX">$LP$ </tex-math></inline-formula> model when predicting speech signals. In particular, the <inline-formula> <tex-math notation="LaTeX">$BF$ </tex-math></inline-formula> approach has several advantages, including fewer parameters, a lower computational complexity, and accurate characterization of speakers. These advantages ensure the utility of the <inline-formula> <tex-math notation="LaTeX">$BF$ </tex-math></inline-formula> model in speech processing applications.

Bloomfield Model Based Signal Process for Networks