SBIR Phase I: Tools for Information Retrieval and Document Classification Using Fast Phonetic Word-Spotting Technology

这个小型企业创新研究第一阶段的研究项目将进行必要的研究和开发，以极大地提高快速语音单词检索器的信息检索能力。完成的研究将为低质量的电话、音频或多媒体数字资源的口述文件检索和分类带来新的方法。口述文档检索一直是广播新闻领域研究较多的问题。然而，存在许多用户必须检索和分类音频质量较低的文档的应用程序。最常用的方法包括将音频流或文件转换为假想的单词序列(语音到文本或STT)，然后使用基于文本的信息检索。虽然这已被证明对广播新闻文档检索是有效的，但这也有缺点。例如，STT对语言模型的明确使用将假设的单词序列限制在其词典内。另一方面，语音匹配能够识别不在词典中的关键字的可能实例，例如名称。STT方法的一个优点是基于文本的信息检索方法的适用性，这种方法在错误率相当小的高质量音频上工作得很好。然而，在大容量电话通道上需要更好的解决方案，其中计算负担和低精度使得STT不切实际。拟议项目的目标是研究和开发基于语音的文档检索和分类算法。基于语音搜索的检索系统的适用性将在现有的大型语料库上进行比较。这项研究的关键创新是使搜索技术适用于有音频而没有文本的环境。从科学上讲，算法必须在概率框架下工作，因为语音单词识别总是基于置信度衡量。在商业上，现有的多媒体或音频档案将可用于数据挖掘。此外，文件类型的决定(例如，给呼叫中心的电话是投诉吗？)在市场情报、安全分析、质量分析和任何呼叫分离应用中的开放商业应用。

This Small Business Innovation Research Phase I research project will perform the research and development necessary to greatly enhance the information retrieval capability of a fast phonetic word-spotter. The completed research will lead to new methods for spoken document retrieval and classification on low quality telephony audio or multimedia digital sources. Spoken document retrieval has been a well-researched problem in the domain of broadcast news. However, many applications exist where users must retrieve and classify documents with lower quality audio. The most commonly applied method involves converting an audio stream or file into a hypothesized sequence of words (Speech-to-Text or STT), and subsequently using text- based information retrieval. Although this has been shown to be effective for broadcast news document retrieval, this has drawbacks. For example, STT's explicit use of language models limits the hypothesized word sequences to those within its lexicon. On the other hand, phonetic matching is capable of identifying likely instances of keywords, such as names, which are not in a lexicon. One advantage of the STT approach is the applicability of text-based information retrieval methods, which work well on high quality audio where the error rates are fairly small. However, better solutions are necessary over a high volume telephony channel where the computational burden and low accuracy make STT impractical. The goal of the proposed project is to research and develop phonetic-based document retrieval and classification algorithms. The applicability of retrieval systems based on phonetic searches will be compared on large existing corpora.The key innovation of the proposed research is to adapt search techniques to function in environments where audio exists, but text does not. Scientifically, algorithms must be made to work in a probabilistic framework, since phonetic word spotting is always based on confidence measures. Commercially, existing multimedia or audio archives will be available for data mining. In addition, decisions of document type (e.g., was the phone call to the call center a complaint?) open commercial applications in market intelligence, security analysis, quality analysis, and any call segregation application.