Research on ranked retrieval of spoken con-tent has assumed the existence of some auto-mated (word or phonetic) transcription. Re-cently, however, methods have been demon-strated for matching spoken terms to spoken content without the need for language-tuned transcription. This paper describes the first application of such techniques to ranked re-trieval, evaluated using a newly created test collection. Both the queries and the collection to be searched are based on Gujarati produced naturally by native speakers; relevance assess-ment was performed by other native speak-ers of Gujarati. Ranked retrieval is based on fast acoustic matching that identifies a deeply nested set of matching speech regions, cou-pled with ways of combining evidence from those matching regions. Results indicate that the resulting ranked lists may be useful for some practical similarity-based ranking tasks.
