document: Two-stream indexing for spoken web search This paper presents two-stream processing of audio to index the audio content for Spoken Web search . The first stream indexes the meta-data associated with a particular audio document . The meta-data is usually very sparse , but accurate . This therefore results in a high-precision , low-recall index . The second stream uses a novel language-independent speech recognition to generate text to be indexed . Owing to the multiple languages and the noise in user generated content on the Spoken Web , the speech recognition accuracy of such systems is not high , thus they result in a low-precision , high-recall index . The paper attempts to use these two complementary streams to generate a combined index to increase the precision-recall performance in audio content search . The problem of audio content search is motivated by the real world implication of the Web in developing regions , where due to literacy and affordability issues , people use Spoken Web which consists of interconnected VoiceSites , which have content in audio . The experiments are based on more than 20,000 audio documents spanning over seven live VoiceSites and four different languages . The results suggest significant improvement over a meta-data-only or a speech-recognitiononly system , thus justifying the two-stream processing approach . Audio content search is a growing problem area and this paper wishes to be a first step to solving this at a large scale , across languages , in a Web context .
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