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Automatic Speaker Segmentation using Multiple Features and Distance Measures: A Comparison of Three Approaches.

, , , , and . ICME, page 1101-1104. IEEE Computer Society, (2006)

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Predicting dialogue success, naturalness, and length with acoustic features., , and . ICASSP, page 5010-5014. IEEE, (2017)Systematic comparison of BIC-based speaker segmentation systems., , , and . MMSP, page 66-69. IEEE, (2007)Speaker-independent negative emotion recognition., , and . CIP, page 417-422. IEEE, (2010)Musical Instrument Classification using Non-Negative Matrix Factorization Algorithms and Subset Feature Selection., , and . ICASSP (5), page 221-224. IEEE, (2006)Speaker segmentation and clustering, , and . Signal Processing, 88 (5): 1091-1124 (2008)Applying Supervised Classifiers Based on Non-negative Matrix Factorization to Musical Instrument Classification., , and . ICME, page 2105-2108. IEEE Computer Society, (2006)Crowd-Sourced Collection of Task-Oriented Human-Human Dialogues in a Multi-domain Scenario., , , , and . TSD, volume 11697 of Lecture Notes in Computer Science, page 398-411. Springer, (2019)A Case Study on the Importance of Belief State Representation for Dialogue Policy Management., , , , and . INTERSPEECH, page 986-990. ISCA, (2018)A Framework for Dialogue Detection in Movies., , , , and . MRCS, volume 4105 of Lecture Notes in Computer Science, page 371-378. Springer, (2006)Prediction of User Emotion and Dialogue Success Using Audio Spectrograms and Convolutional Neural Networks., and . SIGdial, page 336-344. Association for Computational Linguistics, (2019)