Incorporating uncertainty as a Quality Measure in I-Vector Based Language Recognition
|Amir Hossein Poorjam, Rahim Saeidi, Tomi Kinnunen, Ville Hautamäki|
State-of-the-art language recognition systems involve modeling utterances with the i-vectors. However, the uncertainty of the i-vector extraction process represented by the i-vector posterior covariance is affected by various factors such as channel mismatch, background noise, incomplete transformations and duration variability. In this paper, we propose a new quality factor based on the i-vector posterior covariance and incorporate it into the recognition process to improve the recognition accuracy. The experimental results with LRE15 database and various duration conditions show a 2.81% relative improvement in terms of average performance cost as a result of incorporating the proposed quality measure in language recognition systems.