Enhanced Spoken Sentence Retrieval Using a Conventional Automatic Speech Recognizer in Smart Home
With the rapid evolution of smart home environment, the demand for spoken information retrieval (e.g. voice activated FAQ retrieval) on information appliances is increasing. In spoken information retrieval, users' spoken queries are converted into text queries using automatic speech recognition (ASR) engines. If top 1 results of the ASR engines are incorrect, the errors are propagated to information retrieval systems. If a document collection is a small set of sentences such as frequently asked questions (FAQs) the errors have additional effect on the performance of information retrieval systems. To improve the performance of such a sentence retrieval system, we propose a post processing model of an ASR engine. The post processing model consists of a re ranking and a query term generation model. The re ranking model rearranges top n outputs of the ASR engines using the ranking support vector machine (Ranking SVM) The query term generation model extracts meaningful content words from the re ranked queries based on term frequencies and query rankings. In the experiments, the re ranking model improved the top 1 performance results of an underlying ASR engine with 4.4% higher precision and 6.4% higher recall rate. The query term generation model improved the performance results of an underlying information retrieval system with an accuracy 2.4% to 2.6% higher. Based on the experimental result, the proposed model revealed that it could improve the performance of a spoken sentence retrieval system in a restricted domain.
Int. J. Artif. Intell. Tools