An investigation on speaker vector-based speaker identification under noisy conditions

This paper presents the speaker identification method based on a speaker vector under noisy conditions. The aim of this work is to improve the performance of the speaker identification under noisy conditions. The identification system is based on the method of anchor models. In this system, the location of each speaker is represented by the speaker vector which consists of the set of the likelihood between a target utterance and the anchor models. Since the acoustic model of the target is not needed, speaker identification can be performed with a very short reference speech (5.5 sec average). In order to achieve the improvement, the structure of anchor models was investigated. Evaluations were performed on 8 or 30-speaker identification task in Japanese. The results showed that a speaker identification rate of 70.98% has been obtained by using phonetic-class structured GMMs (pcs_GMMs) as anchor models under noisy conditions.