Adaptive conditional pronunciation modeling using articulatory features for speaker verification

This paper proposes an articulatory feature-based conditional pronunciation modeling (AFCPM) technique for speaker verification. The technique models the pronunciation behaviors of speakers by creating a link between the actual phones produced by the speakers and the state of articulations during speech production. Speaker models consisting of conditional probabilities of two articulatory classes are adapted from a set of universal background models (UBMs) using MAP adaptation technique. This adaptation approach aims to prevent over-fitting the speaker models when the amount of speaker data is insufficient for a direct estimation. Experimental results show that the adaptation technique can enhance the discriminating power of speaker models by establishing a tighter coupling between speaker models and the UBMs. Results also show that fusing the scores derived from an AFCPM-based system and a conventional spectral-based system achieves a significantly lower error rate than that of the individual systems. This suggests that AFCPM and spectral features are complementary to each other.