Speaker diarization of meetings can be significantly improved by overlap handling. Several previous works have explored the use of different features such as spectral, spatial and energy for overlap detection. This paper proposes a method to estimate probabilities of speech and overlap classes at a segment level which are later incorporated into an HMM/GMM baseline system. The estimation is motivated by the observation that significant portion of overlaps in spontaneous conversations take place where the amount of silence is less, e.g., during speaker changes. Experiments on the AMI corpus reveal that the probability of occurrence of overlap in a segment is inversely proportional to the amount of silence in it. Whenever this information is combined with acoustic information from MFCC features in an HMM/GMM overlap detector, improvements are verified in terms of F-measure. Furthermore the paper investigates the use of exclusion and labelling strategies based on such detector for handling overlap in diarization reporting F-measure improvements from 0.29 to 0.43 in case of exclusion and from 0.15 to 0.22 in case of labelling. Consequently speaker diarization error is reduced by 8% relative compared to the baseline based solely on acoustic information.