Since there are no systematic pauses delimiting words in speech, the problem of word segmentation is formidable even for monolingual infants. We use computational modeling to assess whether word segmentation is substantially harder in a bilingual than a monolingual setting. Seven algorithms representing different cognitive approaches to segmentation are applied to transcriptions of naturalistic input to young children, carefully processed to generate perfectly matched monolingual and bilingual corpora. We vary the overlap in phonology and lexicon experienced by modeling exposure to languages that are more similar (Catalan and Spanish) or more different (English and Spanish). We find that the greatest variation in performance is due to different segmentation algorithms and the second greatest to language, with bilingualism having effects that are smaller than both algorithm and language effects. Implications of these computational results for experimental and modeling approaches to language acquisition are discussed.

由于在语音中没有系统性的停顿来界定单词，因此即使对于单语婴儿来说，分词问题也很棘手。我们使用计算模型来评估双语环境中的分词是否比单语环境更难。代表不同认知分割方法的七种算法被应用于对幼儿自然输入的转录，经过仔细处理以生成完美匹配的单语和双语语料库。我们通过对更相似的语言（加泰罗尼亚语和西班牙语）或更不同的语言（英语和西班牙语）的接触建模来改变语音和词汇的重叠。我们发现性能差异最大的是由于不同的分割算法，其次是语言，双语的影响小于算法和语言的影响。讨论了这些计算结果对语言习得的实验和建模方法的影响。