| Paper: | SLP-P19.6 | ||
| Session: | Model-based Robust Speech Recognition | ||
| Time: | Friday, May 19, 10:00 - 12:00 | ||
| Presentation: | Poster | ||
| Topic: | Speech and Spoken Language Processing: Model-based robust Speech Recognition | ||
| Title: | Limited Training Data Robust Speech Recognition using Kernel-based Acoustic Models | ||
| Authors: | Martin Schaffoener, Sven Krueger, Edin Andelic, Marcel Katz, Andreas Wendemuth, Otto-von-Guericke University, Germany | ||
| Abstract: | Contemporary automatic speech recognition uses Hidden-Markov-Models (HMMs) to model the temporal structure of speech where one HMM is used for each phonetic unit. The states of the HMMs are associated with state-conditional probability density functions (PDFs) which are typically realized using mixtures of Gaussian PDFs (GMMs). Training of GMMs is error-prone especially if training data size is limited. This paper evaluates two new methods of modelling state-conditional PDFs using probabilistically interpreted Support Vector Machines and Kernel Fisher Discriminants. Extensive experiments on the RM1 corpus yield substantially improved recognition rates compared to traditional GMMs. Due to their generalization ability, our new methods reduce the word error rate by up to 13% using the complete training set and up to 33% when the training set size is reduced. | ||
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