Speaker pruning algorithm for real-time speaker identification

Speaker identification is a computationally expensive task. In this work, we propose an iterative speaker pruning algorithm for speeding up the identification in the context of real-time systems. The proposed algorithm reduces computational load by dropping out unlikely speakers as more data arrives into the processing buffer. The process is repeated until there is just one speaker left in the candidate set. Care must be taken in designing the pruning heuristics, so that the correct speaker will not be pruned. Two variants of the pruning algorithm are presented, and simulations with TIMIT corpus show that an error rate of 10 % can be achieved in 10 seconds for 630 speakers.