Robust Speaker Diarization in a Multi-Speaker Environment Using Autocorrelation-based Noise Subtraction

This paper shows research performed into the topic of speaker diarization for multi-speaker environment. It looks into the algorithms and the implementation of an offline speaker segmentation and indexing system for recorded speech data where usually more than one speaker is present. Speaker diarization is a well studied topic in the domain of broadcast news recordings. Most of the proposed systems involve hierarchical clustering of the data, where the number of speakers and their identities are known a priori. Speaker diarization is the task of assigning a unique label to all speech segments in an audio stream by the same speaker. There are two key challenges: processing speed and robustness in the presence of noise. In this paper we address the robustness issue by using a method already successful in speech recognition application. Using ANS (Autocorrelation-Based Noise Subtraction) for robust genetic algorithm-based speaker diarization, we compare the results with the baseline MFCC-based system in clean and noisy conditions.