Speaker verification based on fusion of acoustic and articulatory information
Ming Li, Jangwon Kim, Prasanta Ghosh, Vikram Ramanarayanan, Shrikanth Shri Narayanan · 2013
We propose a practical, feature-level fusion approach for com-bining acoustic and articulatory information in speaker ver-ification task. We find that concatenating articulation fea-tures obtained from the measured speech production data with conventional Mel-frequency cepstral coefficients (MFCCs) im-proves the overall speaker verification performance. However, since access to the measured articulatory data is impractical for real world speaker verification applications, we also ex-periment with estimated articulatory features obtained using acoustic-to-articulatory inversion technique. Specifically, we show that augmenting MFCCs with articulatory features ob-tained from subject-independent acoustic-to-articulatory inver-sion technique also significantly enhances the speaker verifi-cation performance. This performance boost could be due to the information about inter-speaker variation present in the es-timated articulatory features, especially at the mean and vari-ance level. Experimental results on the Wisconsin X-Ray Mi-crobeam database show that the proposed acoustic-estimated-articulatory fusion approach significantly outperforms the tra-ditional acoustic-only baseline, providing up to 10 % relative re-duction in Equal Error Rate (EER). We further show that we can achieve an additional 5 % relative reduction in EER after score-level fusion. Index Terms: speech production, speaker verification, articula-tion features, acoustic-to-articulatory inversion, biometrics