在时间不变字符串内核 (TISK) 模型的口语词识别的词汇反.
James S Magnuson1,2, Heejo You3, Thomas Hannagan4,5
1BCBL: Basque Center on Cognition, Brain & Language, Donostia-San Sebastián, Spain.
Journal of cognition
|April 29, 2024
概括
时间不变字符串内核 (TISK) 模型现在模拟了自上而下的效果,并添加了词汇反,保持了核心口语识别任务的性能. 这种增强为认知架构提供了高效,强大的处理.
科学领域:
- 认知科学 认知科学
- 计算语言学 计算语言学
- 心理学 心理学 心理学
背景情况:
- 时间不变字符串内核 (TISK) 模型是用于口语识别的交互式激活模型.
- 与TRACE相比,TISK通过使用更少的节点和连接提供了计算效率.
- 原来的TISK模型缺乏词汇反,限制了其模拟上下效应的能力.
研究的目的:
- 研究将词汇反添加到TISK模型的影响.
- 为了确定TISK与反是否可以模拟自上而下的效果,而不会影响现有的性能.
- 评估TISK对带有和没有反的噪音的强度.
主要方法:
- 在TISK模型中实现了词汇反.
- 模拟口语识别任务,包括那些带有噪音的任务.
- 对TISK的性能进行比较,对以前的发现进行反和不进行反.
主要成果:
- 具有词汇反的TISK成功模拟了自上而下的效果.
- 该模型保留了对基本口语识别现象的解释能力.
- 在杂的输入条件下,TISK表现出优雅的降解,并有反.
结论:
- 词汇反增强了TISK模拟认知过程的能力.
- 反在认知架构中为基于约束的强大处理提供了高效的计算基础.
- 有反的TISK模型为建模口语识别提供了一个可行的替代方案.
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