In this paper we present a method for automatically detecting er-roneous training scripts for speech corpora like Broadcast News and Switchboard. Based on the Hub-4 task we will report on the performance of error detection with the proposed method and in-vestigate the effects of both manually and automatically cleaned training corpora on the performance of the RWTH speech recog-nition system. Our approach uses a forced Viterbi alignment on the training data and evaluates different transcription quality measures. The following three criteria proved to be useful to automatically detect most transcription errors: - the difference between the final Viterbi alignment HMM state and the last state according to the transcriptions - the normalized acoustic word scores - the location of the boundary between adjacent segments obtained by forced alignment With manually corrected scripts we achieved a WER reduction on the 1996 HUB-4 eval. corpus. The recognizers performance improved mainly on clean planned speech segments. Whereas the improvements were minor on these hand-transcribed training data, automatic training script verification will become more important for automatically transcribed new speech corpora.