We propose a model of the speech perception of individual words in the presence of mishearings. This phenomenological approach is based on concepts used in linguistics, and provides a formalism that is universal across languages. We put forward an efficient two-parameter form for the word length distribution, and introduce a simple representation of mishearings, which we use in our subsequent modeling of word recognition. In a context-free scenario, word recognition often occurs via anticipation when, part-way into a word, we can correctly guess its full form. We give a quantitative estimate of this anticipation threshold when no mishearings occur, in terms of model parameters. As might be expected, the whole anticipation effect disappears when there are sufficiently many mishearings. Our global approach to the problem of speech perception is in the spirit of an optimization problem. We show for instance that speech perception is easy when the word length is less than a threshold, to be identified with a static transition, and hard otherwise. We extend this to the dynamics of word recognition, proposing an intuitive approach highlighting the distinction between individual, isolated mishearings and clusters of contiguous mishearings. At least in some parameter range, a dynamical transition is manifest well before the static transition is reached, as is the case for many other examples of complex systems.

中文翻译：

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听证会和听证会：解密口语

我们提出了一个在存在误听的情况下对单个单词的语音感知模型。这种现象学方法基于语言学中使用的概念，并提供了一种跨语言通用的形式主义。我们为词长分布提出了一种有效的双参数形式，并引入了误听的简单表示，我们将其用于后续的词识别建模。在无上下文的情况下，单词识别通常通过预期发生，当我们可以正确猜出一个单词的完整形式时。在模型参数方面，我们给出了当没有发生误听时这个预期阈值的定量估计。正如所料，当有足够多的误听时，整个预期效应就会消失。我们解决语音感知问题的全局方法是本着优化问题的精神。例如，我们展示了当词长小于阈值时，语音感知很容易识别为静态转换，否则很难识别。我们将其扩展到单词识别的动态，提出了一种直观的方法，突出了个体、孤立的误听和连续误听集群之间的区别。至少在某些参数范围内，动态转换在达到静态转换之前就已经很明显了，就像复杂系统的许多其他示例一样。我们将其扩展到单词识别的动态，提出了一种直观的方法，突出了个体、孤立的误听和连续误听集群之间的区别。至少在某些参数范围内，动态转换在达到静态转换之前就已经很明显了，就像复杂系统的许多其他示例一样。我们将其扩展到单词识别的动态，提出了一种直观的方法，突出了个体、孤立的误听和连续误听集群之间的区别。至少在某些参数范围内，动态转换在达到静态转换之前就已经很明显了，就像复杂系统的许多其他示例一样。