Combining Gaussianized/Non-Gaussianized Features to Improve Speaker Diarization of Telephone Conversations

We report results on speaker diarization of telephone conversations. This speaker diarization process is similar to the multistage segmentation and clustering system used in broadcast news. It consists of an initial acoustic change point detection algorithm, iterative Viterbi re-segmentation, gender labeling, agglomerative clustering using a Bayesian information criterion (BIC), followed by agglomerative clustering using state-of-the-art speaker identification (SID) methods and Viterbi re-segmentation using Gaussian mixture models (GMMs). We repeat these multistage segmentation and clustering steps twice: once with mel-frequency cepstral coefficients (MFCCs) as feature parameters for the GMMs used in gender labeling, SID, and Viterbi re-segmentation steps and another time with Gaussianized MFCCs as feature parameters for the GMMs used in these three steps. The resulting clusters from the parallel runs are combined in a novel way that leads to a significant reduction in the diarization error rate (DER). On a development set containing 30 telephone conversations, this combination step reduced the DER by 20%. On another test set containing 30 telephone conversations, this step reduced the DER by 13%. The best error rate we have achieved is 6.7% on the development set and 9.0% on the test set.