Speaker Diarization based on Independent Component Analysis



During the last years, sound source separation has been a subject of intense research. In meetings or noisy public places, often a number of speakers are active simultaneously and the sources of interest need to be separated from interfering speech in order to be robustly recognized. An effective algorithm is the Independent Component Analysis (ICA). ICA models the mixture signal as a standard form of linear superposition of source signals. Under difficult environmental conditions, ICA outputs may still contain strong residual components of the interfering speakers. We will use this algorithm for speaker diarization in verification system. We will obtain better results especially in the case of multi-speaker audio datasuch as interview or microphone segment of NIST Speaker Recognition Evaluation.