@InProceedings{Stuhlsatz2007_480,
author = {André Stuhlsatz},
booktitle = {Studientexte zur Sprachkommunikation: Elektronische Sprachsignalverarbeitung 2007},
title = {HSVM - A SVM Toolkit for Segmented Speech Data},
year = {2007},
editor = {Klaus Fellbaum},
month = mar,
pages = {107--114},
publisher = {TUDpress, Dresden},
abstract = {This paper introduces add-on tools aligned with the Hidden-Markov- Model Toolkit (HTK) to use Support Vector Machines (SVM) in speech recognition. The resulting method and tool is named HSVM: Hidden-Markov-Model Toolkit using Support Vector Machines. Because SVMs have proven their generalization performance compared to the common maximum likelihood or MAP approach, a speech recognizer can profit from the use of SVMs for classifying the acoustic features. Given segmented speech data provided by a underlying HMM, the presented tools enable to post-classify acoustic features based on N-best-lists or recognition lattices. HSVM transforms the SVM predictions to probabilities and feeds them back into the recognition process in a offline fashion. Improvements using HSVM are presented for a phoneme classification on the TIMIT corpus as well as Wallstreet Journal Cambridge corpus.},
isbn = {978-3-940046-40-6},
issn = {0940-6832},
keywords = {Spracherkennung},
url = {https://www.essv.de/pdf/pdf/2007_107_114.pdf},
}