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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.