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.
All rights reserved. This work is protected by the corresponding intellectual and industrial property rights. Without prejudice to any existing legal exemptions, reproduction, distribution, public communication or transformation of this work are prohibited without permission of the copyright holder. If you wish to make any use of the work not provided for in the law, please contact: firstname.lastname@example.org