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相关概念视频

Automatic Processing and Automatic Social Behavior01:28

Automatic Processing and Automatic Social Behavior

377
Automatic processing refers to the cognitive operations that occur without conscious intent or awareness, playing a fundamental role in shaping social cognition and behavior. These processes enable individuals to navigate complex social environments efficiently by relying on mental shortcuts and pre-existing knowledge structures known as schemas. One of the most influential mechanisms underlying automatic processing is priming, which subtly activates mental representations through exposure to...
377

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通过多嵌入模型和自动语音识别,先进的口音/方言识别和口音评估.

Shahram Ghorbani1, John H L Hansen1

  • 1Center for Robust Speech Systems (CRSS), The University of Texas at Dallas, Richardson, Texas 75080, USA.

The Journal of the Acoustical Society of America
|June 17, 2024
PubMed
概括

先进的语言和扬声器识别模型提高了口音分类的准确性. 这些系统可靠地评估非母语发音,与人类对语言学习和语音技术进步的感知相关联.

科学领域:

  • 语音处理 语音处理
  • 计算语言学计算语言学
  • 人工智能的人工智能是人工智能.

背景情况:

  • 准确的口音分类和口音评估是具有挑战性的,因为不同的语音变异.
  • 现有的方法在与非母语人士的口音和方言的复杂性作斗争.

研究的目的:

  • 通过使用预训练的语言识别 (LID) 和扬声器识别 (SID) 模型,增强口音分类和非母语口音度评估.
  • 开发一个多嵌入式系统,以提高口音识别 (AID) 的准确性.
  • 调查使用自动语音识别 (ASR) 和AID模型进行客观的口音估计.

主要方法:

  • 从先进预训练的LID和SID模型中利用嵌入.
  • 将LID和SID嵌入式与端到端 (E2E) AID模型集成.
  • 利用在美国英语 (en-US) 上训练的E2E ASR模型和AID模型的en-US输出进行得分.
  • 将客观得分与主观的人类感知得分相关联.

主要成果:

  • 经过预训练的LID和SID模型有效地编码了口音和方言信息.
  • 结合LID,SID和E2E AID嵌入的多嵌入式AID系统可以实现卓越的准确性.
  • ASR错误率和AID模型输出提供可靠的客观重音度得分.

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  • 客观评分显示了相互之间和主观人类评估之间强烈的相关性.

    • 结论:

    • 预训练的LID和SID模型显著改善了口音分类和口音评估.
    • 拟议的多嵌入式AID系统为口音识别提供了更高的准确性.
    • 基于ASR和AID的系统提供了一种可靠和有效的方法,用于客观地估计口音.
    • 这些进步对语言学习,语音可理解性和扬声器识别技术有重大影响.