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关于低资源语音识别技术的前沿研究

Wushour Slam1, Yanan Li1, Nurmamet Urouvas1

  • 1Xinjiang Laboratory of Multi-Language Information Technology, Xinjiang Multilingual Information Technology Research Center, College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China.

Sensors (Basel, Switzerland)
|November 25, 2023
PubMed
概括

本研究回顾了低资源语音识别,重点是通过特征提取和声学模型来提高准确性. 它提出了技术挑战的解决方案,并探索了资源扩展以提高性能.

科学领域:

  • 语音识别技术 语音识别技术
  • 人工智能的人工智能
  • 自然语言处理自然语言处理.

背景情况:

  • 持续的语音识别需要更高的准确性.
  • 低资源语音识别是一个具有挑战性的但有价值的研究领域,因为它的识别率很低.
  • 这项技术在有限的条件下运行,需要专门的方法.

研究的目的:

  • 审查低资源语音识别中的特征提取和声学模型的当前研究状况.
  • 调查资源扩展的方法,以提高识别性能.
  • 确定技术挑战,并为低资源语音识别系统提出解决方案.

主要方法:

  • 关于特征提取技术的文献综述.
  • 对声学模型进步的分析.
  • 资源扩张战略的探索.
  • 识别和提出解决技术挑战的解决方案.

主要成果:

  • 确定了低资源场景的特征提取和声学建模的关键研究趋势.
  • 提出潜在的解决方案,以克服常见的技术障碍.
  • 强调了资源扩展对于提高准确性的重要性.
关键词:
声学模型 声学模型深度特征提取,深度特征提取.低资源的语音识别语言识别.资源扩张 资源扩张

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结论:

  • 低资源语音识别需要集中研究特征提取,声学模型和资源扩展.
  • 解决技术挑战对于实际应用至关重要.
  • 未来的研究应该继续探索创新的方法,以提高有限数据环境中的识别准确性.