Talker Normalization with Articulatory Analysis-by-Synthesis

摘要

Internal articulatory models are used in analysis-by-synthesis to recover the movement of the speech articulators from speech acoustics. The kind of articulatory information that is recovered depends on the application and the available data. In the laboratory some articulatory data may be available along with acoustic data, and in automatic speech recognition only acoustic data is available. While there is more data available in the former than in the latter case, the amount of information sought in recovery is different in the two cases. In the laboratory physically realistic articulatory trajectories are sought, while recovery in automatic speech recognition may simply require transforming the acoustic signal to an abstract articualtory representaion employed by statistical models for subsequent categorization. Both applications require that the internal articulatory models be normalized for each talker, either for realistic recovery or for robust statistical behavior. A method for constructing mappings between the human and the internal model, while simultaneously adjusting the internal model for acoustic matching is presented. The method is tested on x-ray microbeam data taken on human subjects.