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A Survey on Spoken Content Retrieval
Abstract
Spoken information retrieval is the practice of indexing and retrieving spoken material directly from an audio recording rather than through text explanations. It's a feature that transforms audio to text. One of the most significant benefits of speech recognition systems is that they reduce the quantity of misspelt keywords that some typists may experience when typing. A text retrieval engine analyses the ASR output to find relevant information once the spoken content is transcribed into text or lattice format. This architecture is well-suited when the ASR accuracy is sufficiently high. This page details the significant technological contributions made by this research line's theories, concepts, approaches, and successes. It includes two fundamental guidelines: 1) Changed ASR for Retrieval: cascade ASR with text retrieval, but the ASR has been modified or optimised for retrieving spoken content; 2) Interactive Retrieval and Presentation of Retrieved Objects in an Efficient Way: Better retrieval outcomes and user experiences may be obtained by an interactive retrieval technique that incorporates user interactions.
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