Using written language to probe speech recognition models

语音识别模型分为两个主要类别，具有根本不同的处理体系结构。反馈模型(如TRACE，McClelland&Elman，1986)允许词汇知识对音素分析施加自上而下的控制。前馈模型(如Merge，Norris，McQueen&Cutler，2000)假设信息流完全是自下而上的。我们的项目采用了一种创新的方法来测试这些模型类之间的测试，通过检查书面单词知识对语音感知的影响。为了区分不同的模型，对比必须测试不同的加工水平，并检查策略效果。TRACE倾向于具有有限战略影响力的广泛效果；Merge倾向于必然对战略因素敏感的词汇效果

Speech recognition models fall into two principal classes, with fundamentally different processing architectures. Feedback models (e.g. TRACE, McClelland & Elman, 1986) allow lexical knowledge to exert top-down control over phonemic analysis. Feedforward models (e.g. Merge, Norris, McQueen & Cutler, 2000) assume that information flow is entirely bottom-up. Our project adopts an innovative approach to testing between these model classes, by examining the influence of written-word knowledge on speech perception. To distinguish the models, contrasts must test different processing levels and examine strategy effects. TRACE favors broad effects with limited strategic influence; Merge favors lexical effects that are necessarily sensitive to strategic factors