Target-Speaker Voice Activity Detection: a Novel Approach for Multi-Speaker Diarization in a Dinner Party Scenario
Table 2: Diarization results (* stands for DIHARD II reference)
['[EMPTY]', 'DEV DER', 'DEV JER', 'EVAL DER', 'EVAL JER']
Unfortunately, the early versions of TS-VAD did not provide any tangible improvement over the baseline results. So, we firstly focused on improving the baseline diarization. To this end, we applied the improved x-vectors extractor based on a Wide ResNet (WRN), which was trained on the VoxCeleb data. The details on the extractor are given in [Gusev_2020]. Application of the baseline Agglomerative Hierarchical Clustering (AHC) based on Probabilistic Linear Discriminant Analysis (PLDA) scoring to these x-vectors improved DER by about 10% abs. Then we used the idea from [Park_2020] and replaced AHC clustering based on PLDA scores with Spectral Clustering (SC) based on Cosine Similarities. Besides, using WRN x-vectors substantially improved the permutation resolution on the EEND segmentation and covered a large fraction of the gap to the oracle DER. Finally, to improve overall diarization performance, we fused several single-channel and multi-channel TS-VAD models by computing a weighted average of their probability streams. Weights were tuned to minimize DER on the development set.