• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Artificial life and robotics >The long short-term memory based on i-vector extraction for conversational speech gender identification approach
【24h】

The long short-term memory based on i-vector extraction for conversational speech gender identification approach

机译：基于I形式提取的长短期记忆，用于对话语音性别识别方法

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

Stress causes a speaker's voice characteristics to be changed. Emotional stress alters a person's speech pattern such that it is distributed non-normally along the temporal dimension. Thus, the methods for identifying the gender of a non-stressed speaker were no longer effective in recognizing the gender of a speaker in stressful conditions. To address this issue, a new gender identification framework is proposed. We leveraged i-vector for capturing gender information on each speech segment. Then the long short-term memory dynamically handled all speech temporal context features and learned the long-term dependency from the input. We evaluated the effectiveness, in terms of accuracy and the number of iterations to saturate, of the proposed method by comparing it with the baseline methods in their respective abilities to identify the speaker's gender from conversations with different durations. By learning the gender information encoded in long-term dependencies, our proposed method outperforms the baseline methods and is able to correctly identify the speaker's gender in all conversation types.
机译：压力导致扬声器的语音特性要改变。情绪压力改变了一个人的语音模式，使得它沿着时间尺寸不正常地分布。因此，用于识别非压力扬声器的性别的方法不再有效地识别扬声器的性别在压力条件下。为了解决这个问题，提出了新的性别识别框架。我们利用I-vector用于捕获每个语音段的性别信息。然后，长短期内存动态处理所有语音时间上下文特征，并从输入中学到的长期依赖。我们通过将其与基线方法与各自的能力中的基线方法进行比较来评估提出的方法的准确性和迭代次数的有效性，以便将演讲者的性别从不同持续持续时间的对话中识别。通过学习在长期依赖项中编码的性别信息，我们提出的方法优于基线方法，并能够在所有对话类型中正确识别扬声器的性别。

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