• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Computer speech and language >The PASCAL CHiME speech separation and recognition challenge
【24h】

The PASCAL CHiME speech separation and recognition challenge

机译：PASCAL CHiME语音分离和识别挑战

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

Distant microphone speech recognition systems that operate with human-like robustness remain a distant goal. The key difficulty is that operating in everyday listening conditions entails processing a speech signal that is reverberantly mixed into a noise background composed of multiple competing sound sources. This paper describes a recent speech recognition evaluation that was designed to bring together researchers from multiple communities in order to foster novel approaches to this problem. The task was to identify keywords from sentences reverberantly mixed into audio backgrounds binaurally recorded in a busy domestic environment. The challenge was designed to model the essential difficulties of the multisource environment problem while remaining on a scale that would make it accessible to a wide audience. Compared to previous ASR evaluations a particular novelty of the task is that the utterances to be recognised were provided in a continuous audio background rather than as pre-segmented utterances thus allowing a range of background modelling techniques to be employed. The challenge attracted thirteen submissions. This paper describes the challenge problem, provides an overview of the systems that were entered and provides a comparison alongside both a baseline recognition system and human performance. The paper discusses insights gained from the challenge and lessons learnt for the design of future such evaluations.
机译：远距离的麦克风语音识别系统具有类似人的鲁棒性的功能。关键困难在于，在日常聆听条件下进行操作需要处理语音信号，该语音信号被混响混入由多个竞争声源组成的噪声背景中。本文介绍了最近的语音识别评估，该评估旨在将来自多个社区的研究人员召集在一起，以培育针对此问题的新颖方法。任务是从在繁忙的家庭环境中以双耳录制的音频背景中混响的句子中识别关键字。挑战旨在模拟多源环境问题的基本困难，同时保持一定规模，以使广大受众都可以访问。与以前的ASR评估相比，该任务的特殊之处在于，要识别的话语是在连续音频背景中提供的，而不是作为预先分段的话语提供的，因此可以采用多种背景建模技术。挑战吸引了13名参赛者。本文描述了挑战性问题，概述了输入的系统，并与基线识别系统和人员绩效进行了比较。本文讨论了从挑战中获得的见解以及在设计未来此类评估时汲取的教训。

著录项

  • 来源
    《Computer speech and language》 |2013年第3期|621-633|共13页
  • 作者

    Jon Barker; Emmanuel Vincent; Ning Ma; Heidi Christensen; Phil Green;

  • 作者单位

    Department of Computer Science, University of Sheffield, Sheffield SI 4DP, UK;

    INRIA, Centre de Rennes - Bretagne Atlantique, 35042 Rennes Cedex, France;

    Department of Computer Science, University of Sheffield, Sheffield SI 4DP, UK;

    Department of Computer Science, University of Sheffield, Sheffield SI 4DP, UK;

    Department of Computer Science, University of Sheffield, Sheffield SI 4DP, UK;

  • 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 recognition; source separation; noise robustness;

    机译：语音识别;源分离;噪声鲁棒性;

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