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Objective Comparison of Four GMM-Based Methods for PMA-to-Speech Conversion

2016; Springer Science+Business Media; Linguagem: Inglês

10.1007/978-3-319-49169-1_3

1611-3349

Daniel Erro, Inma Hernáez, Luís Serrano, Ibon Saratxaga, Eva Navas,

Music and Audio Processing

In silent speech interfaces a mapping is established between biosignals captured by sensors and acoustic characteristics of speech. Recent works have shown the feasibility of a silent interface based on permanent magnet-articulography (PMA). This paper studies the performance of four different mapping methods based on Gaussian mixture models (GMMs), typical from the voice conversion field, when applied to PMA-to-spectrum conversion. The results show the superiority of methods based on maximum likelihood parameter generation (MLPG), especially when the parameters of the mapping function are trained by minimizing the generation error. Informal listening tests reveal that the resulting speech is moderately intelligible for the database under study.