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TASLP
2016

Speaker and Channel Factors in Text-Dependent Speaker Recognition

4 years 3 months ago
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
Themos Stafylakis, Patrick Kenny, Md. Jahangir Ala
Added 10 Apr 2016
Updated 10 Apr 2016
Type Journal
Year 2016
Where TASLP
Authors Themos Stafylakis, Patrick Kenny, Md. Jahangir Alam, Marcel Kockmann
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