Name
Abstract | ||
|---|---|---|
I-vectors are a concise representation of speaker characteristics. Recent advances in speaker recognition have utilized their ability to capture speaker and channel variability to develop efficient recognition engines. Inter-speaker relationships in the i-vector space are non-linear. Accomplishing effective speaker recognition requires a good modeling of these non-linearities and can be cast as a machine learning problem. In this paper, we propose a kernel partial least squares (kernel PLS, or KPLS) framework for modeling speakers in the i-vectors space. The resulting recognition system is tested across several conditions of the NIST SRE 2010 extended core data set and compared against state-of-the-art systems: Joint Factor Analysis (JFA), Probabilistic Linear Discriminant Analysis (PLDA), and Cosine Distance Scoring (CDS) classifiers. Improvements are shown. |
Year | Venue | Keywords |
|---|---|---|
2011 | INTERSPEECH | kernel partial least squares, speaker recognition, i-vectors |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
9 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Balaji Vasan Srinivasan | 1 | 82 | 14.58 |
| Daniel Garcia-Romero | 2 | 686 | 46.42 |
| Dmitry N. Zotkin | 3 | 171 | 19.06 |
| Ramani Duraiswami | 4 | 1721 | 161.98 |