Intra-conversation intra-speaker variability compensation for speaker clustering

Recently, the speaker clustering approach exploiting the intra-conversation variability in the total variability space has shown promising performance. However, there exists the variability in different segments of the same speaker within a conversation, termed as intra-conversation intra-speaker variability, which may scatter the distribution of the corresponding i-vector based representation of short speech segment, and degrades the clustering performance. To address this issue, we propose a new speaker clustering approach based on an extended total variability factor analysis. In our proposed method, the intra-conversation total variability space is divided into the inter-speaker and intra-speaker variability space. And by explicitly compensating the intra-conversation intra-speaker variability, the short speech segments would be represented more accurately. To evaluate the effectiveness of the proposed method, we conduct extensive experiments on NIST SRE 2008 summed channel telephone dataset. The experimental results show that the proposed method clearly outperforms the other state-of-the-art speaker clustering techniques in terms of clustering error rate.