Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Measures on wavelet segmentation of speech
MUSP'08 Proceedings of the 8th WSEAS International Conference on Multimedia systems and signal processing
Automatic speech segmentation based on acoustical clustering
SSPR&SPR'10 Proceedings of the 2010 joint IAPR international conference on Structural, syntactic, and statistical pattern recognition
Text independent methods for speech segmentation
Nonlinear Speech Modeling and Applications
Improvements on automatic speech segmentation at the phonetic level
CIARP'11 Proceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
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A complete automatic speech segmentation technique has been studied in order to eliminate the need for manually segmented sentences. The goal is to fix the phoneme boundaries using only the speech waveform and the phonetic sequence of the sentences.The phonetic boundaries are established using a Dynamic Time Warping algorithm that uses the a posteriori probabilities of each phonetic unit given the acoustic frame. These a posteriori probabilities are calculated by combining the probabilities of acoustic classes which are obtained from a clustering procedure on the feature space and the conditional probabilities of each acoustic class with respect to each phonetic unit.The usefulness of the approach presented here is that manually segmented data is not needed in order to train acoustic models. The results of the obtained segmentation are similar to those obtained using the HTK toolkit with the "flat-start" option activated. Finally, results using Artificial Neural Networks and manually segmented data are also reported for comparison purposes.