Automatic speech recognition using Hidden Conditional Neural Fields
Automatic speech recognition using Hidden Conditional Neural Fields
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DOI：
10.1109/icassp.2011.5947488
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发表时间：
2011-05
期刊：
影响因子：
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通讯作者：
Yasuhisa Fujii;Kazumasa Yamamoto;S. Nakagawa
中科院分区：
文献类型：
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作者：
Yasuhisa Fujii;Kazumasa Yamamoto;S. Nakagawa
隐藏条件随机场（HCRF）是一种非常有前途的语音建模方法。然而，由于HCRF通过对线性加权特征求和来计算假设的分数，因此它不能考虑对语音识别至关重要的特征之间的非线性。在本文中，我们通过引入神经网络中的门函数对HCRF进行了扩展，并提出了一种新的隐条件神经场模型（HCNF）。与传统方法不同的是，HCNF可以在没有任何初始模型的情况下进行训练，并且可以包含任何类型的特征。在TIMIT核心测试集上的连续音素识别和单声道日语读语音识别任务的实验结果表明，HCNF优于以MPE方式训练的HCRF和HMM。
Hidden Conditional Random Fields(HCRF) is a very promising approach to model speech. However, because HCRF computes the score of a hypothesis by summing up linearly weighted features, it cannot consider non-linearity among features that will be crucial for speech recognition. In this paper, we extend HCRF by incorporating gate function used in neural networks and propose a new model called Hidden Conditional Neural Fields(HCNF). Differently with conventional approaches, HCNF can be trained without any initial model and incorporate any kinds of features. Experimental results of continuous phoneme recognition on TIMIT core test set and Japanese read speach recognition task using monophone showed that HCNF was superior to HCRF and HMM trained in MPE manner.