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Recent neural speech coding methods optimize the coding rates for perceptual performance in an end-to-end manner. In this paper, we build the relationship between perceptual-oriented compression and the concept of "semantic" compression. We propose a synonymity-based semantic speech coding framework, in which synonymous representations corresponding to the extracted latent features serve as the input of the semantic compression. This framework is designed to approach the compression limits established by recent semantic information theory while preserving perceptual qualities. We provide an implementation of our proposed framework using a K-means algorithm to determine the synonymous representations and a nonlinear transform coding model as the semantic compression method to approach the compression limits. Experimental results show that our method outperforms both traditional and neural speech coding schemes, achieving superior compression efficiency and better perceptual quality.