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Corpus-independent history compression for stochastic turn-taking models

机译:随机转弯模型的与语料库无关的历史压缩

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Stochastic turn-taking models use a truncated representation of past speech activity to specify how likely a speaker is to talk at the next instant. An unanswered question in such modeling is how far back to extend the conditioning context. We study this question using Switchboard (English, telephone) and Spontal (Swedish, face-to-face) conversations. We also explore whether to trade off precision with range when moving backward in the history. We find that (1) a nearly logarithmic compression of history is optimal, for both speaker and interlocutor; (2) the absolute duration of the conditioning context is at least 7 seconds; and (3) the compression scheme generalizes remarkably well across the two different corpora.
机译:随机转向模型使用过去语音活动的截断表示来指定说话者在下一瞬间说话的可能性。在这种建模中一个未解决的问题是扩展条件上下文有多远。我们使用总机(英语,电话)和邦特(瑞典,面对面)对话来研究这个问题。我们还探讨了在历史上向后移动时是否要在范围与精度之间进行权衡。我们发现(1)对于说话者和对话者,历史的近对数压缩是最佳的; (2)条件上下文的绝对持续时间至少为7秒; (3)压缩方案在两个不同的语料库中有很好的概括。

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