• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Computer speech and language >Speech segmentation using regression fusion of boundary predictions
【24h】

Speech segmentation using regression fusion of boundary predictions

机译：使用边界预测的回归融合进行语音分割

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

In the present work we study the appropriateness of a number of linear and non-linear regression methods, employed on the task of speech segmentation, for combining multiple phonetic boundary predictions which are obtained through various segmentation engines. The proposed fusion schemes are independent of the implementation of the individual segmentation engines as well as from their number. In order to illustrate the practical significance of the proposed approach, we employ 112 speech segmentation engines based on hidden Markov models (HMMs), which differ in the setup of the HMMs and in the speech parameterization techniques they employ. Specifically we relied on sixteen different HMMs setups and on seven speech parameterization techniques, four of which are recent and their performance on the speech segmentation task have not been evaluated yet. In the evaluation experiments we contrast the performance of the proposed fusion schemes for phonetic boundary predictions against some recently reported methods. Throughout this comparison, on the established for the phonetic segmentation task TIM IT database, we demonstrate that the support vector regression scheme is capable of achieving more accurate predictions, when compared to other fusion schemes reported so far.
机译：在当前的工作中，我们研究了用于语音分割任务的许多线性和非线性回归方法的适用性，这些方法用于组合通过各种分割引擎获得的多个语音边界预测。所提出的融合方案独立于各个分段引擎的实现以及它们的数量。为了说明所提出方法的实际意义，我们使用了基于隐马尔可夫模型（HMM）的112个语音分割引擎，它们在HMM的设置及其所采用的语音参数化技术方面有所不同。具体来说，我们依赖于16种不同的HMM设置以及7种语音参数化技术，其中4种是最新技术，其在语音分割任务上的性能尚未得到评估。在评估实验中，我们将针对语音边界预测提出的融合方案的性能与最近报道的方法进行了对比。在整个比较中，在为语音分割任务TIM IT数据库建立的过程中，我们证明了与迄今为止报道的其他融合方案相比，支持向量回归方案能够实现更准确的预测。

著录项

  • 来源
    《Computer speech and language》 |2010年第2期|273-288|共16页
  • 作者

    Iosif Mporas; Todor Ganchev; Nikos Fakotakis;

  • 作者单位

    Artificial Intelligence Group, Wire Communications Laboratory, Department of Electrical and Computer Engineering, University of Patras, GR-26500, Greece;

    Artificial Intelligence Group, Wire Communications Laboratory, Department of Electrical and Computer Engineering, University of Patras, GR-26500, Greece;

    Artificial Intelligence Group, Wire Communications Laboratory, Department of Electrical and Computer Engineering, University of Patras, GR-26500, Greece;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    speech segmentation; regression fusion; hidden markov models;

    机译：语音分割回归融合隐藏的马尔可夫模型;

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号