Recently, we proposed an i-vector approach to acoustic sniffing for irrelevant variability normalization based acoustic model training in large vocabulary continuous speech recognition (LVCSR). Its effectiveness has been confirmed by experimental results on Switchboard-1 conversational telephone speech transcription task. In this paper, we study several discriminative feature extraction approaches in i-vector space to improve both recognition accuracy and run-time efficiency. New experimental results are reported on a much larger scale LVCSR task with about 2000 hours training data.