The nature of statistical learning theory
The nature of statistical learning theory
Making large-scale support vector machine learning practical
Advances in kernel methods
Time Map Phonology: Finite State Models and Event Logics in Speech Recognition
Time Map Phonology: Finite State Models and Event Logics in Speech Recognition
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Phonetic feature extraction for context-sensitive glottal source processing
Speech Communication
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Generally speech recognition systems make use of acoustic features as a representation of speech for further processing. These acoustic features are usually based on human auditory perception or signal processing. More recently, Articulatory Feature (AF) based speech representations have been investigated by a number of speech technology researchers. Articulatory features are motivated by linguistic knowledge and hence may better represent speech characteristics. In this paper, we introduce two popular classification models, Hidden Markov Model (HMM) and Support Vector Machine (SVM), for automatic articulatory feature extraction. HMM-based systems are found to be best when there is good balance in the numbers of positive and negative examples in the data while SVM is better in the unbalanced data condition.