对海量语音进行基于内容的检索需要语音识别技术和检索技术的结合。本文通过调节语言模型的途径研究在不同识别率的语音识别文本上进行关键词检索的差异,由此研究语音识别性能和检索性能之间的关联性。通过对114小时语音数据的实验表明:语音识别性能与检索性能有一定的相关性,同时也说明改进检索的方法可以消除一部分由于语音识别所带来的误差。研究结果为进一步针对性地改进识别引擎、语音识别输出的表示和相应的快速检索方法提供了基础。

It is a paradigm to integrate speech recognition and information retrieval techniques to implement content-based retrieval in mass speech data. The paper studies the relationship between speech recognition performance and retrieval performance, through analyzing the differences of keywords retrieval in the recognition documents with different recognition rates, which are adjusted by the language models. The experiment on 114 hours speech data indicates: speech recognition performance has some correlation with retrieval performance, and illuminates that improving the retrieval method can eliminate some speech recognition errors. The result provides the basis for further advancements in speech recognition engine, speech recognition results representation and rapid retrieval method.