Advances in the Automatic Transcription of Lectures

Transcribing lectures is a challenging task, both in acoustic and in language modeling. In this work, we present recent results on the automatic transcription of lectures from the Translanguage English Database, which contains the recordings of talks given in English at Eurospeech `93, by mostly non-native speakers. Concerning acoustic modeling, the acoustic model trained for a broadcast news transcription task was adapted on the lectures training data through Maximum Likelihood Linear Regression adaptation, including models of spontaneous speech phenomena. Moreover, a normalization procedure was embodied in the training stage, consisting in a cluster-based mean and variance normalization of the static features. Language modeling was based on adaptation of a background language model estimated on broadcast news transcripts, conference proceedings, lecture transcripts, and conversational speech transcripts. Among the examined adaptation techniques, the most effective one was obtained by exploiting the paper presented in each lecture to be processed. The best transcription performance on a 2 hours test set was 32.4% word error rate