...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Journal of network and computer applications >Robust several-speaker speech recognition with highly dependable online speaker adaptation and identification
【24h】

Robust several-speaker speech recognition with highly dependable online speaker adaptation and identification

机译：强大的多说话者语音识别功能以及高度可靠的在线说话者自适应和识别功能

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

The currently adaptive mechanisms adapt a single acoustic model for a speaker in speaker-independent speech recognition system. However, as more users use the same speech recognizer, single acoustic model adaptation leads to negative adaptation upon switching between users. Such a situation is problematic (undependable adaptation). This paper, considering the situation of a smart home or an office with staff members, presents the speaker-specific acoustic model adaptation based on a multi-model mechanism, to solve the problem of undependable adaptation. First, the identification of the current speaker is confirmed using the SVM classifier, then the corresponding acoustic parameters are extracted and integrated with the speaker-independent acoustic model to yield the speaker-dependent acoustic model and speech recognition accuracy then be promoted for the current speaker. To provide dependable adaptation data to achieve online positive speaker adaptation, a mechanism that measures confidence score is designed to verify each recognition result and determined whether it can be an adaptation datum. The experimental results indicate that the proposed system can effectively increase the average speech recognition accuracy from 62% to 85%. Thus, the proposed system can achieve robust several-speaker speech recognition with highly dependable online speaker adaptation and identification.
机译：当前的自适应机制在独立于说话者的语音识别系统中为说话者适应单个声学模型。但是，随着更多的用户使用相同的语音识别器，单个声学模型的适应会导致用户之间切换时产生负面的适应。这种情况是有问题的（适应性很差）。本文考虑了智能家居或职员办公的情况，提出了一种基于多模型机制的说话人特定的声学模型自适应方法，以解决自适应性不可靠的问题。首先，使用SVM分类器确认当前说话人的身份，然后提取相应的声学参数，并将其与独立于说话人的声学模型集成，以得出独立于说话人的声学模型，然后提高当前说话人的语音识别精度。为了提供可靠的适应数据以实现在线正说话者适应，设计了一种用于测量置信度得分的机制，以验证每个识别结果并确定其是否可以作为适应数据。实验结果表明，该系统可以有效地将平均语音识别准确率从62％提高到85％。因此，所提出的系统可以通过高度可靠的在线说话者自适应和识别来实现鲁棒的多个说话者语音识别。

著录项

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