title: Retrieving Tract Variables From Acoustics: A Comparison of Different Machine Learning Strategies.
abstract: 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.
authors: The Department of Electrical and Computer Enginering, Institute of Systems Research, University of Maryland, College Park, MD 20742 USA.
authors: Vikramjit
authors: V
authors: Mitra
authors: Hosung
authors: H
authors: Nam
authors: Carol Y
authors: CY
authors: Espy-Wilson
authors: Elliot
authors: E
authors: Saltzman
authors: Louis
authors: L
authors: Goldstein