Making large-scale support vector machine learning practical
Advances in kernel methods
Speech Communication - Special issue on interactive voice technology for telecommunication applications
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Integration of Tone Related Feature for Chinese Speech Recognition
ICMI '02 Proceedings of the 4th IEEE International Conference on Multimodal Interfaces
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Tonal languages, such as Chinese, use systematic variations of pitch to distinguish lexical or grammatical meaning. Thus, tone recognition is essential for tonal languages. Typically, tone recognition for isolated syllables involves three major steps: fundamental frequency (F0) detection, feature extraction, and classification. The work compares different techniques for these three steps and to answer the questions: for Mandarin Chinese syllables, what combination of fundamental frequency detection and feature extraction methods best prepare data for classification, and what is the most effective classification method for tone recognition. Three types of F0 detection methods (autocorrelation, cross-correlation and cepstrum), two feature extraction schemes (sampled F0 and average F0, slope and energy from three subsegments), four normalization methods (slope only, 0--100 scaled, z-score and T1 shift), and two classification methods (Support Vector Machine (SVM) and Multilayer Perceptron (MLP)) were experimentally studied using 700 collected data samples.