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相关实验视频

Updated: Jun 27, 2025

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
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基于深度学习的定制土耳其自动语音识别系统,由语言模型支持.

Yasin Görmez1

  • 1Management Information System, Sivas Cumhuriyet University, Sivas, Merkez, Turkiye.

PeerJ. Computer science
|April 25, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了土耳其语自动语音识别的深度学习模型,通过整合语言模型显著提高了准确性. 与现有文献相比,开发的系统表现出卓越的性能.

关键词:
自动语音识别自动语音识别深度学习是一种深度学习.机器学习是机器学习.序列到序列模型的序列.土耳其语音识别系统单词规范化中的一个词.

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科学领域:

  • 自然语言处理自然语言处理.
  • 机器学习 机器学习
  • 语音技术 语言技术

背景情况:

  • 自动语音识别 (ASR) 系统对于日常应用至关重要,但在土耳其语等不那么常见的语言中落后.
  • 土耳其的结合性结构给ASR系统的开发带来了独特的挑战.
  • 现有的ASR研究还没有充分解决土耳其语的需求.

研究的目的:

  • 为土耳其自动语音识别提出先进的深度学习模型.
  • 通过语言模型集成,提高土耳其语的ASR性能.
  • 在ASR中解决土耳其语的特殊语言挑战.

主要方法:

  • 开发了结合卷积神经网络,GRU,LSTM和变压器层的深度学习模型.
  • 利用Zemberek库构建一种用于性能增强的语言模型.
  • 应用贝叶斯优化用于微调深度学习模型的超参数.
  • 使用标准指标评估性能:文字错误率 (WER) 和字符错误率 (CER).

主要成果:

  • 在没有语言模型的情况下,WER/CER分数为22.2/14.05 (土耳其麦克风语音库) 和11.5/4.15 (土耳其语音库).
  • 通过语言模型集成,分数提高到9.85/5.35 (土耳其麦克风语音库) 和8.4/2.7 (土耳其语音库).
  • 提出的模型在现有文献基准中表现出优越的性能.

结论:

  • 通过语言建模和优化的超参数增强的深度学习模型显著提高了土耳其ASR准确性.
  • 开发的方法为土耳其语等形态丰富的语言提供了ASR的强有力的解决方案.
  • 这项研究有助于为资源不足的语言推进语音识别技术.