Abstract

We propose a practical, feature-level and score-level fusion approach by combining acoustic and estimated articulatory information for both text independent and text dependent speaker verification. From a practical point of view, we study how to improve speaker verification performance by combining dynamic articulatory information with the conventional acoustic features. On text independent speaker verification, we find that concatenating articulatory features obtained from measured speech production data with conventional Mel-frequency cepstral coefficients (MFCCs) improves the performance dramatically. However, since directly measuring articulatory data is not feasible in many real world applications, we also experiment with estimated articulatory features obtained through acoustic-to-articulatory inversion. We explore both feature level and score level fusion methods and find that the overall system …

Date

2016

Authors

Ming Li, Jangwon Kim, Adam Lammert, Prasanta Kumar Ghosh, Vikram Ramanarayanan, Shrikanth Narayanan

Journal

Computer speech & language

Volume

36

Pages

196-211

Publisher

Academic Press