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.
CitationButko, T.; Nadeu, C. A hierarchical architecture with feature selection for audio segmentation in a broadcast news domain. A: Jornadas en Tecnología del Habla and Iberian SLTech Workshop. "VI Jornadas en Tecnología del Habla and II Iberian SLTech Workshop". 2010, p. 429-432.
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