Name
Abstract | ||
|---|---|---|
In this paper, we propose a speaker verification system called m-vector system, where speakers are represented by uniform segmentation of their Maximum Likelihood Linear Regression (MLLR) super-vectors, denoted m-vectors. The MLLR super-vectors are extracted with respect to Universal Background Model (UBM) with MLLR adaptation using the speakers data. Two criterion are followed to segment the MLLR super-vector: one is disjoint segmentation technique and other one is overlapped windows. Afterward, m-vectors are conditioned by our recently proposed [1] session variability compensation algorithm before calculating score during test phase. However, the proposed method is not based on any total variability space concept and uses simple MLLR transformation for extracting m-vector without considering any transcription of the speech segment. The proposed system shows promising performance compared to the conventional i-vector system. This indicates that session variability compensation plays an important role in speaker verification. Speakers can be represented by simpler way instead of generating i-vector in conventional system and able to achieve performance comparable to the i-vector based system. Experiment results are shown on NIST 2008 SRE core condition. |
Year | Venue | Keywords |
|---|---|---|
2012 | European Signal Processing Conference | m-vector,MLLR super-vector,LDA,WCCN,Speaker Verification |
Field | DocType | ISSN |
Speaker verification,Disjoint sets,Pattern recognition,Regression analysis,Segmentation,Computer science,Speech recognition,NIST,Speaker recognition,Maximum likelihood linear regression,Artificial intelligence,Compensation algorithm | Conference | 2076-1465 |
Citations | PageRank | References |
0 | 0.34 | 1 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Achintya Kumar Sarkar | 1 | 23 | 7.81 |
| Jean-François Bonastre | 2 | 493 | 36.03 |
| Driss Matrouf | 3 | 404 | 41.80 |