Unified and Optimal Unit-Selection Framework

Abstract

This chapter is devoted to the class of unit-selection algorithms proposed by us earlier, and which represent an optimal and unified generalization over the single-frame and sub-optimal segmental unit-selection algorithms of Lee and Cox dealt with in the previous chapter. Following a detailed treatment of this unified formulation, we present the modified one-pass DP algorithm which provides an optimal solution to this unified formulation, and characterize its performance in terms of rate-distortion curves obtained using large unit-database size. We first show how it generalizes over the single-frame algorithm towards handling longer fixed length units, with progressively enhanced rate-distortion performance with increase in unit-size. Subsequently, we compare the rate-distortion performance of the proposed algorithm with the sub-optimal segmental algorithm of Lee and Cox and demonstrate the clear advantages accrued. Following this, we answer the question of what, if any, is the advantage of moving from small clustered segment codebook, as in the classic Shiraki and Honda’s variable length quantization algorithm, to large unit-database sizes as in the unit-selection framework, and through rate-distortion performances, show the highly enhanced performances of the unit-selection algorithms in comparison to the conventional vector quantization, matrix quantization and variable length segment quantization using clustered codebooks.