Estimating Speaker Height and Subglottal Resonances Using MFCCs and GMMs

This letter investigates the use of MFCCs and GMMs for 1) improving the state of the art in speaker height estimation, and 2) rapid estimation of subglottal resonances (SGRs) without relying on formant and pitch tracking (unlike our previous algorithm in [1]). The proposed system comprises a set of height-dependent GMMs modeling static and dynamic MFCC features, where each GMM is associated with a height value. Furthermore, since SGRs and height are correlated, each GMM is also associated with a set of SGR values (known a priori). Given a speech sample, speaker height and SGRs are estimated as weighted combinations of the values corresponding to the N most-likely GMMs. We assess the importance of using dynamic MFCC features and the weighted decision rule, and demonstrate the efficacy of our approach via experiments on height estimation (using TIMIT) and SGR estimation (using the Tracheal Resonance database.