Resumen en español

Acoustic-to-Articulatory inversion, which seeks to estimate an articulator position using the acoustic information in the speech signal, offers several potential applications in the field of speech processing. In this context, it is important to use acoustic parameters with the ability to increase the performance of acoustic-to-articulatory inversion systems. This paper analyzes the importance of formants as inputs to such inversion systems from an analytical and a statistical perspective. The former is based on an articulatory synthesizer that simulates the voice signal from the vocal tract. The statistical analysis is based on real data provided by an electromagnetic articulograph, for which we estimate the statistical association between acoustic features and articulator movement. As a measure of statistical association, the information measure is utilized. The results are tested on a neuralnetwork- based Acoustic-to-Articulatory inversion system. The use of formants as inputs led to an improvement of 2.2% and 2.8% in the root-mean-square error and correlation values, respectively.