Content-based Language Models for Spoken Document Retrieval

Abstract

Spoken document retrieval (SDR) has been extensively studied in recent years because of its potential use in navigating large multimedia collections in the near future. This paper presents a novel concept of applying content-based language models to spoken document retrieval. In an example task for retrieval of Mandarin Chinese broadcast news data, the content-based language models, either trained on automatic transcriptions of spoken documents or adapted from baseline language models using automatic transcriptions of spoken documents, were used to create more accurate recognition results and indexing terms from both spoken documents and speech queries. We report on some interesting findings obtained in this research.