11th Annual Conference of the International Speech Communication Association

Makuhari, Chiba, Japan
September 26-30. 2010

Fast Computation of Speaker Characterization Vector Using MLLR and Sufficient Statistics in Anchor Model Framework

Achintya Kumar Sarkar, S. Umesh

IIT Madras, India

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.

Full Paper

Bibliographic reference.  Sarkar, Achintya Kumar / Umesh, S. (2010): "Fast computation of speaker characterization vector using MLLR and sufficient statistics in anchor model framework", In INTERSPEECH-2010, 2738-2741.