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Graph-Based Partial Hypothesis Fusion for Pen-Aided Speech Input

机译:基于图的部分假设融合用于笔式语音输入

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摘要

We study a specific partial hypothesis fusion problem in sequential data labeling. The problem arises in the multimodal applications where a decision is made by merging complete hypothesis from one input and partial hypothesis from the other. For example, in a pen-aided speech interface, appropriate pen input can provide partial but crucial information. We address the problem in a Bayesian framework, and reformulate the solution as a revised search in a representation. A dynamic programming algorithm is proposed to efficiently solve the partial hypothesis fusion via the graph. It is shown that the computational cost of the graph based partial hypothesis fusion is proportional to the size of the graph, which is highly feasible for a given compact graph. We apply the proposed algorithm to two real applications: an intelligent pen-based dictation error correction system and an automatic handwritten character completion with a speech “shortcut”. Experimental results show that the algorithm is effective in utilizing the partial information from one modality to enhance the bimodal interface performance.
机译:我们研究顺序数据标签中的特定部分假设融合问题。问题出现在多模式应用程序中,其中通过合并一个输入的完整假设和另一输入的部分假设来做出决策。例如,在笔辅助语音界面中,适当的笔输入可以提供部分但至关重要的信息。我们在贝叶斯框架中解决该问题,并将解决方案重新表述为表示形式中的修订搜索。提出了一种动态规划算法,通过图有效地解决了部分假设融合问题。结果表明,基于图的部分假设融合的计算成本与图的大小成正比,这对于给定的紧凑图是高度可行的。我们将提出的算法应用于两个实际应用:基于笔的智能听写纠错系统和带有语音“快捷键”的自动手写字符补全。实验结果表明,该算法有效地利用了一种模态的部分信息,提高了双模态接口的性能。

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