Automatic transcription of lecture speech using topic-independent language modeling
Automatic transcription of lecture speech using topic-independent language modeling
复制标题
使用主题无关的语言模型自动转录讲座演讲
DOI：
10.21437/icslp.2000-40
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发表时间：
2000
期刊：
影响因子：
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通讯作者：
Tatsuya Kawahara
中科院分区：
文献类型：
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作者：
Kazuomi Kato;H. Nanjo;Tatsuya Kawahara
我们的方法讲座语音识别与topicindependent语言模型及其适应。由于演讲有其不同于报纸和谈话的独特风格，需要专门的语言建模。问题是，虽然讲座有许多特定于主题和领域的关键字，但每个领域的可用语料库的大小有限。因此，我们引入主题无关的建模与词汇选择机制的基础上的互信息标准。与传统的基于词频的方法相比，该方法具有更好的覆盖率和准确率，且计算复杂度小。这个基线模型是适应于特定的讲座使用预印文本。我们尝试了口头报告的自动转录，平均单词错误率为23.6%。
We approach lecture speech recognition with a topicindependent language model and its adaptation. As lecture speech has its characteristic style that is different from newspapers and conversations, dedicated language modeling is needed. The problem is that, although lectures have many keywords specific to the topic and fields, available corpus of each domain is limited in size. Thus, we introduce topic-independent modeling with a vocabulary selection mechanism based on a mutual information criterion. It realizes better coverage and accuracy with small complexity than the conventional word frequency-based method. This baseline model is adapted to specific lectures using preprint texts. We have tried automatic transcription of oral presentations and achieved a word error rate of 23.6% on the average.
DOI：
10.21437/icslp.2000-852
发表时间：
2000-10
期刊：
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影响因子：
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作者：
Tatsuya Kawahara;Akinobu Lee;Tetsunori Kobayashi;K. Takeda;N. Minematsu;S. Sagayama;K. Itou;Akinori Ito;Mikio Yamamoto;A. Yamada;T. Utsuro;K. Shikano
通讯作者：
Tatsuya Kawahara;Akinobu Lee;Tetsunori Kobayashi;K. Takeda;N. Minematsu;S. Sagayama;K. Itou;Akinori Ito;Mikio Yamamoto;A. Yamada;T. Utsuro;K. Shikano