Learning Tract Variables with Distal Supervised Learning Model

Ying Chen; Shaobai Zhang
February 2013
Journal of Networks;Feb2013, Vol. 8 Issue 2, p397
Academic Journal
This paper compared the performance of tract variables (TVs) estimation with pellet trajectory estimation by using the trajectory mixture density networks (TMDN), and the result indicated that TVs can be estimated more accurately than the pellet trajectories. We used eight tract variables as articulatory information to model speech dynamics, and parameterized the speech signal as melfrequency cepstral coefficients (MFCCs) and acoustic parameters (APs), and then we analyzed TV estimation using the distal supervised learning (DSL) model. For the DSL, we first analyzed its theoretical foundation and then proposed a global optimization approach for its inversion model. The results of the experiment showed that distal supervised learning has a good estimation performance for tract variables, so it plays an important role in speech inversion and gesture-based ASR architecture.


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