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用机器学习对自动转录进行后处理,以进行口头流利度评分.

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此摘要是机器生成的。

机器学习的自动语音识别 (ASR) 可以准确地评分语言流任务. 虽然有希望,但对于完全自动化,可靠的口语流性评估,需要进一步改进ASR.

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科学领域:

  • 认知心理学 认知心理学
  • 计算语言学 计算语言学
  • 语音技术 语言技术

背景情况:

  • 语言流性任务对于评估认知功能至关重要.
  • 准确的转录与单词时序对于评分这些任务至关重要.
  • 手动转录是耗时和劳动密集的.

研究的目的:

  • 通过机器学习增强的自动语音识别 (ASR) 来比较手工转录的口语流度得分.
  • 评估使用机器学习来提高ASR用于口语流度评分的可行性.

主要方法:

  • 1400个人的口语流利记录 (动物和字母F任务) 通过亚马逊网络服务自动转录.
  • 自动转录被手动纠正,以创建"黄金标准"的转录.
  • 机器学习分类器被训练来区分有效和无效的语句,以实现自动评分.

主要成果:

  • 机器学习分类器在两个任务中都实现了有效和无效语句的良好分离.
  • 来自自动转录的口头流利度得分与手动校正的得分有很高的相关性.
  • 一些得分呈现出较低的相关性,表明需要改善的ASR领域.

结论:

  • 机器学习可以增强现成的ASR,用于评分口语流度单词列表.
  • 自动推导的口语流度得分可能适合某些应用.
  • 对于可靠的,完全自动化的口语流性评估,需要在ASR方面取得进一步的进展.