This review gives a general overview of techniques used in statistical parametric speech synthesis. One instance of these techniques, called hidden Markov model (HMM)-based speech synthesis, has recently been demonstrated to be very effective in synthesizing acceptable speech. This review also contrasts these techniques with the more conventional technique of unit-selection synthesis that has dominated speech synthesis over the last decade. The advantages and drawbacks of statistical parametric synthesis are highlighted and we identify where we expect key developments to appear in the immediate future.

This paper proposes a new generative model which can deal with rotational data variations by extending Separable Lattice 2-D HMMs (SL2D-HMMs). In image recognition, geometrical variations such as size, location and rotation degrade the performance, therefore normalization is required. SL2D-HMMs can perform an elastic matching in both horizontal and vertical directions; this makes it possible to model invariances to size and location. To deal with rotational variations, we introduce additional HMM states which represent the shifts of the state alignments of the observation lines in a particular direction. Face recognition experiments show that the proposed method improves the performance significantly for rotational variation data.