...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Multimedia Tools and Applications >A novel approach to perform context-based automatic spoken document retrieval of political speeches based on wavelet tree indexing
【24h】

A novel approach to perform context-based automatic spoken document retrieval of political speeches based on wavelet tree indexing

机译：基于小波树索引的基于语境的自动口语文献检索的新方法

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Spoken document retrieval for a specific context is a very trending and interesting area of research. It makes it convenient for users to search through archives of speech data, which is not possible manually as it is very time consuming and expensive. In the current article, we focus on performing the same for political speeches, delivered in a variety of environments. The technique used here takes an archive of spoken documents (audio files) as input and performs automatic speech recognition (ASR) on it to derive the textual transcripts, using deep neural networks (DNN), hidden markov models (HMM) and Gaussian mixture models (GMM). These transcriptions are further pruned for indexing by applying certain pre-processing techniques. Thereafter, it builds time and space efficient index of the documents using wavelet trees for its retrieval. The constructed index is searched through to find the count of occurrences of the words in the query, fired by the users. These counts are then utilized to calculate the term frequency - inverse document frequency (TF-IDF) scores, and then the similarity score of the query with each document is calculated using cosine similarity method. Finally, the documents are ranked based on these scores in the order of relevance. Therefore, the proposed system develops a speech recognition system and introduces a novel indexing scheme, based on wavelet trees for retrieving data.
机译：特定上下文的口头文档检索是一个非常趋势和有趣的研究领域。它使用户可以通过语音数据的档案进行方便，这是手动无法手动的，因为它非常耗时和昂贵。在目前的文章中，我们专注于对政治演讲的表现相同，在各种环境中提供。这里使用的技术将口头文档（音频文件）作为输入，以输入的自动语音识别（ASR）用于使用深度神经网络（DNN），隐藏的Markov模型（HMM）和高斯混合模型来导出文本成绩单（GMM）。通过应用某些预处理技术进一步修剪这些转录以进行分度。此后，它为使用小波树为其检索构建了文件的时间和空间有效索引。搜索构建的索引来查找查询中的单词的出现差，由用户触发。然后利用这些计数来计算术语频率 - 逆文档频率（TF-IDF）分数，然后使用余弦相似性方法计算每个文档的查询的相似度得分。最后，根据相关性的顺序，根据这些分数排序。因此，所提出的系统开发语音识别系统并基于用于检索数据的小波树来介绍一种新颖的索引方案。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号