Finite impulse response (FIR) filters for speech analysis and synthesis

Speech was analyzed using a cosine basis vector set to model speech spectral magnitude. This basis vector set is quite similar to an optimal principal-components basis vector set. However, in contrast with the principal components, under some conditions the cosine basis vector coefficients can be used to directly determine an FIR speech synthesis filter. Depending on the details of how the magnitude spectrum is first scaled in frequency and in amplitude before the cosine basis vector modeling, different forms of FIR filters are obtained. Speech was synthesized using the FIR filters obtained from discrete cosine coefficients and compared with speech synthesized from a principal-components-based vocoder, and with speech synthesized from a linear predictive vocoder. For a fixed number of synthesis filter parameters, the FIR synthesis with a minimum phase characteristic was nearly as good as synthesis based on the optimal principal components and about the same as linear predictive synthesis.