Speaker- and Text-Independent Estimation of Articulatory Movements and Phoneme Alignments from Speech
This work addresses the problem of integrating speech analysis tasks for applications in speech processing and synthesis, though it is incremental by combining existing tasks.
The paper tackled the joint task of estimating articulatory movements and phoneme alignments from speech in a speaker- and text-independent manner, achieving a mean correlation of 0.73 for articulatory inversion and up to 87% frame overlap compared to a text-dependent aligner.
This paper introduces a novel combination of two tasks, previously treated separately: acoustic-to-articulatory speech inversion (AAI) and phoneme-to-articulatory (PTA) motion estimation. We refer to this joint task as acoustic phoneme-to-articulatory speech inversion (APTAI) and explore two different approaches, both working speaker- and text-independently during inference. We use a multi-task learning setup, with the end-to-end goal of taking raw speech as input and estimating the corresponding articulatory movements, phoneme sequence, and phoneme alignment. While both proposed approaches share these same requirements, they differ in their way of achieving phoneme-related predictions: one is based on frame classification, the other on a two-staged training procedure and forced alignment. We reach competitive performance of 0.73 mean correlation for the AAI task and achieve up to approximately 87% frame overlap compared to a state-of-the-art text-dependent phoneme force aligner.