Introducing a new information retrieval method applicable for speech recognized texts

In this article a pre-processing method is introduced which is applicable in speech recognized texts retrieval task. We have a text corpus that generated from a speech recognition system and a query as inputs, want to search queries in these documents and find relevant documents. The main problem is that the typical speech recognized texts suffer from some percentage of recognition error. This problem causes terms to have erroneously assign to irrelevant documents.
The idea of our proposed method, is to detect error-prone terms and to find similar words for each term. A parameter is defined which calculate the probability for occurring error in the error-prone words. To recognize similar words for each specific term, based on a criterian which is called average detection rate (ADR) and levenshtein distance criterion, some candidates are chosen as the initial similar words set. Then, a conversion probability is defined based on the conversion rate (CR) and the noisy channel model (NCM) and the words with higher probability based on a threshold level are selected as the final similar words. In the retrieval process, these words are considered in the search step in addition to the base word. Implementation result shows a significant improvement up to 30% in F-measure in information retrieval method with consideration this pre-processing.