Speaker Diarization using Embedding Vectors

In recent years, with the rapid increase of voice data, solutions are being sought extensively for examination and indexing in the field of speech processing. One of these solutions is the speaker diarization, which is used to examine speech records that include multi-speaker. The speaker diarization system basically splits the speech file into segments using the speech file's silence fields and examines the similarity between the segments. In this study, deep learning based embedding vectors are used for speaker representation. The vector embedding proposed for speaker representation is performed with x-vectors extracted using time-delayed deep neural network and d-vectors extracted using LSTM. Then the system is tested with xd-vectors consisting of the combination of these two vectors. As a result, the effect of representative embedding vectors on the performance of the diarization system is examined in the scope of this paper.