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Retrieving Tract Variables From Acoustics: A Comparison of Different Machine Learning Strategies

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5 Author(s)
Vikramjit Mitra ; Dept. of Elec. & Comput. Eng., Institute of Systems Research, University of Maryland, College Park, MD, USA ; Hosung Nam ; Carol Y. Espy-Wilson ; Elliot Saltzman
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Many different studies have claimed that articulatory information can be used to improve the performance of automatic speech recognition systems. Unfortunately, such articulatory information is not readily available in typical speaker-listener situations. Consequently, such information has to be estimated from the acoustic signal in a process which is usually termed “speech-inversion.” This study aims to propose and compare various machine learning strategies for speech inversion: Trajectory mixture density networks (TMDNs), feedforward artificial neural networks (FF-ANN), support vector regression (SVR), autoregressive artificial neural network (AR-ANN), and distal supervised learning (DSL). Further, using a database generated by the Haskins Laboratories speech production model, we test the claim that information regarding constrictions produced by the distinct organs of the vocal tract (vocal tract variables) is superior to flesh-point information (articulatory pellet trajectories) for the inversion process.

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IEEE Journal of Selected Topics in Signal Processing  (Volume:4 ,  Issue: 6 )