Bayesian vocal tract model estimates of nasal stops for speaker verification

In this paper we report on speaker verification experiments using branched vocal tract model estimates of alveolar nasal (/n/) stops. While the discriminatory potential of nasal acoustics has long been established, their acoustic properties have so far mostly been characterized using spectral features. Here, we used a Bayesian estimation technique to obtain reflection coefficients of a branched-tube model of the combined nasal and oral tract. Parameters were then modeled using probabilistic linear discriminant analysis to calculate likelihood ratios for speaker verification trials. Performance was assessed on normal and high vocal effort speech using high-quality and mobile-telephone-transmitted recordings taken from the German-language Pool2010 corpus. Results are compared with those of systems based on mel-frequency cepstral coefficients (MFCC). Vocal tract parameter based systems outperform MFCC based systems in matched conditions, but lack robustness under mismatch, while being readily interpretable with respect to a physical speech production model.