A hybrid architecture for automatic segmentation of speech waveforms

被引：10

作者：

Mporas, Iosif ^{[1
]}

Ganchev, Todor ^{[1
]}

Fakotakis, Nikos ^{[1
]}

机构：

[1] Univ Patras, Dept Elect & Comp Engn, Artificial Intelligence Grp, Wire Commun Lab, Rion 26500, Greece

来源：

2008 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING, VOLS 1-12 | 2008年

关键词：

speech segmentation; hidden Markov models; embedded training; isolated-unit training;

D O I：

10.1109/ICASSP.2008.4518645

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

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%.

引用

页码：4457 / 4460

页数：4

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