Speaker diarization is the task of identifying ``who spoke when'' in an audio stream containing multiple speakers. This is an unsupervised task as there is no a priori information about the speakers. Diagnostical studies on state-of-the-art diarization systems have isolated three main issues with the systems; overlapping speech, effects of background noise and speech/nonspeech detection errors on clustering, and signficant performance variance between different systems. In this thesis we focuss on addressing these issues in diarization. We propose new features based on structure of a conversation such as silence and speaker change statistics for overlap detection. The features are estimated from a long-term context (3-4 seconds) and are used to estimate the probability of overlap at a given instant. These probabilities are later incorporated into acoustic feature based overlap detector as prior probabilities. Experiments on several meeting corpora reveal that overlap detection is improved significantly by the proposed method and this consequently reduces the diarization error. To address the issues arising from background noise, errors in speech/non-speech detection and capture speaker discriminative information in the signal, we propose two methods. In the first method, we propose Information Bottleneck with Side Information (IBSI) based diarization to supress artefacts of background noise and non-speech segments introduced into clustering. In the second method, we show that the phoneme transcript of a given recording carries useful information for speaker diarization. This obervation was used in estimation of phoneme background model which is used for diarization in Information Bottleneck (IB) framework. Both the methods achieve significant reduction in error on various meeting corpora. We train different artificial neural network (ANN) architectures to extract speaker discriminant features and use these features as input to speaker diarization systems. The ANNs are trained to perform related tasks such as speaker comparison, speaker classification and auto encoding. The bottleneck layer activations from these networks are used as features for speaker diarization. Experiments on different meeting corpora revealed that combination of MFCCs and ANN features reduces the diarization error. To address the issue of performance variations across different sytems, we propose feature level combination of HMM/GMM and IB diarization systems. The combination does not require any changes to the original systems. The output of IB system is used to generate features which when combined with MFCCs in a HMM/GMM system reduce diarization error.