Sixth European Conference on Speech Communication and Technology
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 recognizer’s 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.
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Bibliographic reference. Pitz, Michael / Molau, Sirko (1999): "Automatic verification of broadcast news transcriptions", In EUROSPEECH'99, 675-678.