Reference speaker selection for kinematic-independent acoustic-to-articulatory-inversion

Abstract

This paper investigates the most effective reference speaker set for the Parallel Reference Speaker Weighting (PRSW) algorithm for kinematic-independent acoustic-to-articulatory inversion. To obtain the adaptation weights for estimating the articulatory model, different reference speaker accent types and quantities have been acquired. The reference speaker sets have been selected not only based on their performance in speaker-dependent kinematic-inversion but also based on the type of accent. The experiments have been conducted on parallel Acoustic-Articulatory data, the Marquette Electromagnetic Articulography corpus of Mandarin Accented English (EMA-MAE) consisting of 20 native English speakers and 19 native Mandarin speakers of English. A comparison is made between different types of target speakers and reference speakers with results indicating that the accuracy of the adapted model increases when we select balanced distributed accents of English and lower number of speakers.