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In audio and speech processing, accurate detection of the changing points between multiple speakers in speech segments is an important stage for several applications such as speaker identification and tracking. Bayesian Infor- mation Criteria (BIC)-based approaches are the most traditionally used ones as they proved to be very effective for such task. The main criticism levelled against BIC-based approaches is the use of a penalty parameter in the BIC function. The use of this parameters consequently means that a fine tuning is required for each variation of the acoustic conditions. When tuned for a certain condition, the model becomes biased to the data used for training limiting the model's general- isation ability. In this paper, we propose a BIC-based tuning-free approach for speaker seg- mentation through the use of ensemble-based learning. A forest of segmentation trees is constructed in which each tree is trained using a sampled version of the speech segment. During the tree construction process, a set of randomly selected points in the input sequence is examined as potential segmentation points. The point that yields the highest ΔBIC is chosen and the same process is repeated for the resultant left and right segments. The tree is constructed where each node corresponds to the highest ΔBIC with the associated point index. After building the forest and using all trees, the accumulated ΔBIC for each point is calcu- lated and the positions of the local maximums are considered as speaker changing points. The proposed approach is tested on artificially created conversations from the TIMIT database. The approach proposed show very accurate results compara- ble to those achieved by the-state-of-the-art methods with a 9% (absolute) higher F1 compared with the standard ΔBIC with optimally tuned penalty parameter.

Utilising Tree-Based Ensemble Learning for Speaker Segmentation