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In stochastic coding, the speech signal is represented as the product code of several acoustically independent vector elements. The short-time spectral and pitch redundancies in the speech signal are modeled by a set of time-varying linear filters. The unpredictable part of the speech signal (residual) is modeled by a codebook of white innovation signals. Schroeder and Atal had shown the effectiveness of probabilistically generated codes in providing the optimum innovation sequence in each block. However, the computational complexity of exhaustive search block coding using stochastic codebooks is extremely high. We describe in this paper speech coding using efficient pseudostochastic block codes. The pseudo-stochastic codes refer to stochastically populated block codes in which the adjacent codewords in an innovation codebook are non-independent. The pseudostochastic codewords are constructed so as to maximize their "complementarity." We discuss efficient exhaustive search procedures for determining the minimum-error pseudo-stochastic code in a block. These procedures exploit the dependencies of the adjacent codewords to efficiently obtain the ensemble of filter outputs. The computational cost of stochastic coding can be reduced by more than an order of magnitude using the pseudo-stochastic block codes.