Speaker characterization using long-term and temporal information
Chien-Lin Huang, Hanwu Sun, Bin Ma, Haizhou Li · 2010
This paper presents new techniques for front-end analysis using long-term and temporal information for speaker recognition. We propose a long-term feature analysis strategy that averages short-time spectral features over a period of time in an effort to capture the speaker traits that are manifested over a speech segment longer than a spectral frame. We found that the moving averages of temporal information are effective in speaker recognition as well. The experiments on the 2008 NIST Speaker Recognition Evaluation dataset show the longterm and temporal information contribute to substantial EER reductions.