SPEECH RECOGNITION BASED ON SUBWORD UNITS
SPEECH RECOGNITION BASED ON SUBWORD UNITS
复制标题
基于子词单元的语音识别
DOI：
10.1541/ieejeiss1987.118.4_520
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发表时间：
1996
影响因子：
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通讯作者：
S. Taniguchi
中科院分区：
文献类型：
--
作者：
Mikio Mori;T. Koizumi;A. Fukuyama;S. Taniguchi
词汇量大、孤立的词识别需要大量与词汇量成正比的训练数据来表征每个单独的词模型。基于子词单元的方法比基于词的方法更可行，可以克服训练数据大小的问题，因为不同的词在前者的表示中可以共享共同的段。本文研究了几个孤立的词识别系统，这些系统通常采用基于子词单元的方法，尽管它们的分割方法完全不同。在一个系统中，使用隐马尔可夫模型将单词分解为子词单元（片段），并将这些子词单元的频谱馈送到递归神经网络以产生该单词的子词编码序列。这个序列然后被一组孤立单词的隐马尔可夫模型识别为原始单词。在另一种系统中，通过查找delta的峰值来检测单词内的子词边界。
Large vocabulary, isolated word recognition requires a large amount of training data proportional to the vocabulary size to characterize each individual word model. A subword‐unit‐based approach is a more viable alternative than the word‐based approach to overcome the problem of the training data size, since different words can share common segments in their representations in the former. This paper deals with a couple of isolated word recognition systems where the subword‐unit‐based approach is commonly employed, though their methods of segmentation are completely different. In one system a hidden Markov model is used to decompose a word into subword units (segments), and frequency spectra of those subword units are fed to a recurrent neural network to yield a subword code sequence for the word. This sequence is then recognized hopefully as the original word by a set of hidden Markov models for isolated words. In the other system subword boundaries within a word are detected by finding peaks of the delta...