A hybrid architecture for automatic segmentation of speech waveforms

In the present work, we propose a hybrid architecture for automatic alignment of speech waveforms and their corresponding phone sequence. The proposed architecture does not exploit any phone boundary information. Our approach combines the efficiency of embedded training techniques and the high performance of isolated-unit training. Evaluating on the established for the task of phone segmentation TIMIT database, we achieved an accuracy of 83.56%, which corresponds to improving the baseline system's accuracy by 6.09%.