Automatic Segmentation of Speech at the Phonetic Level
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.
KeywordsGaussian Mixture Model Automatic Segmentation Dynamic Time Warping Posteriori Probability Test Sentence
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