MLNov 20, 2015
Variational Bayes Factor Analysis for i-Vector Extraction
arXiv:1511.07422v1
Originality Synthesis-oriented
AI Analysis
This work addresses incremental improvements in speaker recognition for systems with limited development data.
The paper tackles the problem of overfitting and data scarcity in i-vector extraction by deriving equations for a Variational Bayes i-vector extractor, enabling longer i-vectors and adaptation between databases.
In this document we are going to derive the equations needed to implement a Variational Bayes i-vector extractor. This can be used to extract longer i-vectors reducing the risk of overfittig or to adapt an i-vector extractor from a database to another with scarce development data. This work is based on Patrick Kenny's joint factor analysis and Christopher Bishop's variational principal components.