Source-normalised-and-weighted LDA for robust speaker recognition using i-vectors

The recently developed i-vector framework for speaker recognition has set a new performance standard in the research field. An i-vector is a compact representation of a speaker utterance extracted from a low-dimensional total variability subspace. Prior to classification using a cosine kernel, i-vectors are projected into an LDA space in order to reduce inter-session variability and enhance speaker discrimination. The accurate estimation of this LDA space from a training dataset is crucial to classification performance. A typical training dataset, however, does not consist of utterances acquired from all sources of interest (ie., telephone, microphone and interview speech sources) for each speaker. This has the effect of introducing source-related variation in the between-speaker covariance matrix and results in an incomplete representation of the within-speaker scatter matrix used for LDA. Proposed is a novel source-normalised-and-weighted LDA algorithm developed to improve the ro...

• Type: Journal
• Year: 2011
• Where: ICASSP
• Authors: Mitchell McLaren, David A. van Leeuwen
• Keywords: ICASSP 2011, LDA Space, Signal Processing, Speaker Recognition, Training Dataset