JFA modeling with left-to-right structure and a new backend for text-dependent speaker recognition

This paper introduces a new formulation of Joint Factor Analysis (JFA) for text-dependent speaker recognition based on left-to-right modeling with tied mixture HMMs. It accommodates many different ways of extracting multiple features to characterize speakers (features may or may not be HMM state-dependent, they may be modeled with subspace or factorial priors and these priors maybe imputed from text-dependent or text-independent background data). We feed these features to a new, trainable classifier for text-dependent speaker recognition in a manner which is broadly analogous to the i-vector/PLDA cascade in text-independent speaker recognition. We have evaluated this approach on a challenging proprietary dataset consisting of telephone recordings of short English and Urdu pass-phrases collected in Pakistan. By fusing results obtained with multiple front ends, equal error rate of around 2% are achievable.