Anchor modeling technique is shown to be useful in reducing computational complexity for speaker identification and indexing of large audio database,where speakers are projected onto a talker space spanned by a set of pre-defined anchor models represented by GMMs.The characterization of each speaker involves likelihood calculation with each anchor models and is therefore expensive even in the GMM-UBM frame work using top-C mixtures scoring.An computationaly efficient method is proposed here to calculate the likelihood of speech utterances using anchor speaker-specific MLLR matrix and sufficient statistics estimated from the utterance.Since anchor models use distance measures to identify speakers, they are used as a first stage to select N probable speakers and then cascaded by a conventional GMM-UBM system which finally identifies the speaker from this reduced set.The proposed method is 4.21x faster than the conventional cascade anchor system with comparable performance on NIST-04 SRE.