Speaker and Channel Factors in Text-Dependent Speaker Recognition

—We reformulate Joint Factor Analysis so that it can serve as a feature extractor for text-dependent speaker recognition. The new formulation is based on left-to-right modeling with tied mixture HMMs and it is designed to deal with problems such as the inadequacy of subspace methods in modeling speaker-phrase variability, UBM mismatches that arise as a result of variable phonetic content, and the need to exploit text-independent resources in text-dependent speaker recognition. We pass the features extracted by factor analysis to a trainable backend which plays a role analogous to that of PLDA in the i-vector/PLDA cascade in text-independent speaker recognition. We evaluate these methods on a proprietary dataset consisting of English and Urdu passphrases collected in Pakistan. By using both text-independent data and text-dependent data for training purposes and by fusing results obtained with multiple front ends

• Type: Journal
• Year: 2016
• Where: TASLP
• Authors: Themos Stafylakis, Patrick Kenny, Md. Jahangir Alam, Marcel Kockmann
• Keywords: Software Engineering, TASLP 2016