Adaptive score fusion using Weighted Logistic Linear Regression for spoken language recognition

Abstract

State-of-the-art spoken language recognition systems typically consist of a combination of sub-systems. These sub-systems generate language detection scores for each speech segment, which will be fused (combined) to yield the overall detection scores. Typically, score fusion is achieved using a linear model and Logistic Linear Regression (LLR) is commonly used to estimate the model parameters. This paper proposes an extension to the LLR model, known as the Weighted LLR (WLLR). WLLR is obtained using a weighted combination of multiple LLRs where the weights are obtained as a nonlinear function of the speech segments. Although the resultant score is still linear with respect to the scores of the individual sub-systems, the linear function depends on the speech segment. Hence, the overall score fusion model can be regarded as an adaptive model. Experimental results shows that WLLR outperforms LLR by approximately 10% relative for PPRLM system fusion on the NIST 2003 and 2005 language recognition evaluation sets.