论文标题
部分可观测时空混沌系统的无模型预测
Alternate Intermediate Conditioning with Syllable-level and Character-level Targets for Japanese ASR
论文作者
论文摘要
储层计算是预测湍流的有力工具，其简单的架构具有处理大型系统的计算效率。然而，其实现通常需要完整的状态向量测量和系统非线性知识。我们使用非线性投影函数将系统测量扩展到高维空间，然后将其输入到储层中以获得预测。我们展示了这种储层计算网络在时空混沌系统上的应用，该系统模拟了湍流的若干特征。我们表明，使用径向基函数作为非线性投影器，即使只有部分观测并且不知道控制方程，也能稳健地捕捉复杂的系统非线性。最后，我们表明，当测量稀疏、不完整且带有噪声，甚至控制方程变得不准确时，我们的网络仍然可以产生相当准确的预测，从而为实际湍流系统的无模型预测铺平了道路。
End-to-end automatic speech recognition directly maps input speech to characters. However, the mapping can be problematic when several different pronunciations should be mapped into one character or when one pronunciation is shared among many different characters. Japanese ASR suffers the most from such many-to-one and one-to-many mapping problems due to Japanese kanji characters. To alleviate the problems, we introduce explicit interaction between characters and syllables using Self-conditioned connectionist temporal classification (CTC), in which the upper layers are ``self-conditioned'' on the intermediate predictions from the lower layers. The proposed method utilizes character-level and syllable-level intermediate predictions as conditioning features to deal with mutual dependency between characters and syllables. Experimental results on Corpus of Spontaneous Japanese show that the proposed method outperformed the conventional multi-task and Self-conditioned CTC methods.
