Abstract
A new way of determining correlation coefficients for stochastic codebook vectors for CELP coding of speech takes advantage of the sparsely populated nature of stochastic codebook vectors. N valued input signals (e.g., convolution vectors) to be correlated with N valued codebook vectors are fed to an N by N multiplexer or other selection means and the signal values either passed to an accumulator or not according to the state of N select inputs or other identification means determined from a memory store (e.g., an EPROM) whose entries correspond to the non-zero values of the codebook vectors. The accumulator output is the correlation of the codebook vector with the input signal. A sequencer steps through the entire codebook to provide correlation values for each vectors. The results are used to determine the optimum stochastic codebook vector for replicating the particular speech frame being analyzed.
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