A formulation of the autoregressive HMM for speech synthesis
Matt Shannon, Bill Byrne · Apollo (University of Cambridge) · 2009
We present a formulation of the autoregressive HMM for speech synthesis and compare it to the standard HMM synthesis framework and the trajectory HMM. We give details of how to do efficient parameter estimation and synthesis with the autoregressive HMM and discuss consequences of the autoregressive HMM model. There are substantial similarities between the three models, which we explore. The advantages of the autoregressive HMM are that it uses the same model for parameter estimation and synthesis in a consistent way, in contrast to the standard HMM synthesis framework, and that it supports easy and efficient parameter estimation, in contrast to the trajectory HMM.