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Over the past 35 years, it has been established that mental representations of language include fine-grained acoustic details stored in episodic memory. The empirical foundations of this fact were established through a series of word recognition experiments showing that participants were better at remembering words repeated by the same talker than words repeated by a different talker (talker-specificity effect). This effect has been widely replicated, but exclusively with isolated, generally monosyllabic, words as the object of study. Whether fine-grained acoustic detail plays a role in the encoding and retrieval of larger structures, such as spoken sentences, has important implications for theories of language understanding in natural communicative contexts. In this study, we extended traditional recognition memory methods to use full spoken sentences rather than individual words as stimuli. Additionally, we manipulated attention at the time of encoding in order to probe the automaticity of fine-grained acoustic encoding. Participants were more accurate for sentences repeated by the same talker than by a different talker. They were also faster and more accurate in the Full Attention than in the Divided Attention condition. The specificity effect was more pronounced for the Divided Attention than the Full Attention group. These findings provide evidence for specificity at the sentence level. They also highlight the implicit, automatic encoding of fine-grained acoustic detail and point to a central role for cognitive resource allocation in shaping memory-based language representations.