The International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET 2018)

23 / 2017-12-31 05:15:16

Robust Speaker Diarization for news broadcast

speaker diarization,clustering,voice active detection,speaker segmentation

This contribution presents an efficient method of speaker diarization that employs bayesian information criterion for speaker embeddings. In contrast to the traditional approaches
the speaker segmentation is done using manually spectral features. The proposed method is capable enough to segment audio recording of a broadcast news by i-vectors as well as GMM
speaker model and the conventional GMM based agglomerative for clustering the data. An unsupervised Voice Active Detector (VAD) has been developed, so that it could distinguish between speech frame and non-speech frame such that the non-speech frames can be discarded. The results of our proposed method showed significantly outperformed with the benchmark methods
and reduced the diarization error margin by 14%.