Evaluation of linear regression for speaker adaptation in HMM-based articulatory movements estimation

Acoustic-to-articulatory inversion problem is usually studied in speaker-specific manner because both articulatory data and acoustic features contain speaker-specific components. This paper presents our work on speaker-adaptation training for this problem. We implement speaker adaptation in HMM-based acoustic-to-articulatory inversion mapping, and evaluate different combinatorial structures of the articulatory data and acoustic features. The HMM-based inversion mapping models are built with single-stream and multistream, independent clustering and shared clustering structures. The speaker adaptation is implemented in stream-independent structure and shared adaptation structure. The constrained maximum likelihood linear regression method is used for the speaker-adaptive transformation. The experimental results show that the sharing of the speaker-adaptive transformation of the articulatory feature stream and acoustic feature stream can improve the estimation accuracy in inversion mapping. The multi-stream system with shared clustering and shared adaptive transformation has the best result among all the tested structures.