Voice mimic system using an articulatory codebook for estimation of vocal tract shape

Voice mimic systems using articulatory codebooks require an initial estimate of the vocal tract shape in the vicinity of the global optimum. For this purpose, we need to gather a large set of corresponding articulatory and acoustic data in the articulatory codebook. Thus, searching and accessing the codebook becomes a di cult task. In this paper, the design of an articulatory codebook is presented where an acoustic network sub-samples the acoustic space such that vocal tract model shapes are ordered and clustered in the network according to acoustic parameters. Another issue addressed in this paper concerns estimating the trajectory of vocal tract shapes as they change with time. Since the inverse mapping from acoustic parameters to model shape does not have a unique solution, several vocal tract shape variations are possible. Therefore, a dynamic optimization of trajectories has been developed. This optimization uses dynamic properties of each articulatory parameter to estimate the next position.