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Modelling the uncertainty in recovering articulation from acoustics

机译：对从声学中恢复发音的不确定性进行建模

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摘要

This paper presents an experimental comparison of the performance of the multilayer perceptron (MLP) with that of the mixture density network (MDN) for an acoustic-to-articulatory mapping task. A corpus of acoustic-articulatory data recorded by electromagnetic articulography (EMA) for a single speaker was used as training and test data for this purpose. In theory, the MDN is able to provide a richer, more flexible description of the target variables in response to a given input vector than the least-squares trained MLP. Our results show that the mean likelihoods of the target articulatory parameters for an unseen test set were indeed consistently higher with the MDN than with the MLP. The increase ranged from approximately 3% to 22%, depending on the articulatory channel in question. On the basis of these results, we argue that using a more flexible description of the target domain, such as that offered by the MDN, can prove beneficial when modelling the acoustic-to-articulatory mapping.
机译：本文介绍了多层感知器（MLP）和混合物密度网络（MDN）在声学到发音映射任务中的性能的实验比较。为此目的，将由电磁关节造影（EMA）记录的针对单个说话者的声学发音数据集用作训练和测试数据。从理论上讲，与最小二乘训练的MLP相比，MDN能够响应给定的输入矢量，提供更丰富，更灵活的目标变量描述。我们的结果表明，对于一个看不见的测试集，目标关节参数的平均可能性确实比MLP一致地更高。根据所讨论的咬合通道，增加幅度约为3％至22％。根据这些结果，我们认为，在对声音到发音映射进行建模时，使用更灵活的目标域描述（例如MDN提供的描述）可以证明是有益的。

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