A Hierarchical Architecture with Feature Selection for Audio Segmentation in a Broadcast News Domain

This work presents a hierarchical HMM-based audio segmentation system with feature selection designed for the Albayzin 2010 Evaluations. We propose an architecture that combines the outputs of individual binary detectors which were trained with a specific class-dependent feature set adapted to the characteristics of each class. A fast one-pass-training wrapper-based technique was used to perform a feature selection and an improvement in average accuracy with respect to using the whole set of features is reported.