Automatic Transcription Verification of Broadcast News and Similar Speech Corpora

In the last few years, the focus in ASR research has shifted from the recognition of clean read speech (i.e. WSJ) to the more challenging task of transcribing found speech like broadcast news (Hub-4 task) and telephone conversations (Switchboard). Available training corpora tend to become larger and more erroneous than before, as transcribing found speech is more difficult. In this paper we present a method to automatically detect faulty training scripts. Based on the Hub-4 task we will report on the efficiency of error detection with the proposed method and investigate the effect of both manually and automatically cleaned training corpora on the word error rate (WER) of the RWTH large vocabulary continuous speech recognition (LVCSR) system. This work is a joint effort of the University of Technology (RWTH) and Philips Research Laboratories Aachen, Germany.