While speaker identification performance has improved dramatically over the past years, the presence of interfering noise and the variety of channel conditions pose a major obstacle. Particularly the mismatch between training and test condition leads to severe performance degradations. In this paper we investigate speaker identification based on data simultaneously recorded with multiple microphones in a far-field setup under different noise and reverberation conditions. Dramatic performance degradation is observed, especially when training and test conditions mismatch. To address this mismatch we apply our robust frame-based score competition approach in which we combine and compete models trained on multiple conditions. To further improve this approach we add simulated, i.e. artificially created training data on a variety of noise conditions for additional model training. Our experimental results show that the extended approach significantly improves speaker identification performance under adverse and mismatching conditions.
Bibliographic reference. Jin, Qin / Schultz, Tanja (2008): "Robust far-field speaker identification under mismatched conditions", In INTERSPEECH-2008, 1893-1896.