title: Improved speech inversion using general regression neural network.
abstract: The problem of nonlinear acoustic to articulatory inversion mapping is investigated in the feature space using two models, the deep belief network (DBN) which is the state-of-the-art, and the general regression neural network (GRNN). The task is to estimate a set of articulatory features for improved speech recognition. Experiments with MOCHA-TIMIT and MNGU0 databases reveal that, for speech inversion, GRNN yields a lower root-mean-square error and a higher correlation than DBN. It is also shown that conjunction of acoustic and GRNN-estimated articulatory features yields state-of-the-art accuracy in broad class phonetic classification and phoneme recognition using less computational power.
authors.affiliation: Institute for Intelligent Systems, and Department of Electrical and Computer Engineering, 3815 Central Avenue, The University of Memphis, Memphis, Tennessee 38152, USA snajnin@memphis.edu, bbnerjee@memphis.edu.
authors.forename: Shamima
authors.initials: S
authors.lastname: Najnin
authors.affiliation: Institute for Intelligent Systems, and Department of Electrical and Computer Engineering, 3815 Central Avenue, The University of Memphis, Memphis, Tennessee 38152, USA snajnin@memphis.edu, bbnerjee@memphis.edu.
authors.forename: Bonny
authors.initials: B
authors.lastname: Banerjee
mesh_terms: D003198:Computer Simulation; D016208:Databases, Factual; D005260:Female; D006801:Humans; D008297:Male; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D009716:Numerical Analysis, Computer-Assisted; D010363:Pattern Recognition, Automated; D010700:Phonetics; D012044:Regression Analysis; D015203:Reproducibility of Results; D013061:Speech Acoustics; D013068:Speech Production Measurement / Q000379:methods*