Improving broadcast news transcription with a precision grammar and discriminative reranking

Abstract

We propose a new approach of integrating a precision grammar into speech recognition. The approach is based on a novel robust parsing technique and discriminative reranking. By reranking 100-best output of the LIMSI German broadcast news transcription system we achieved a significant reduction of the word error rate by 9.6% relative. To our knowledge, this is the first significant improvement for a real-world broad-domain speech recognition task due to a precision grammar. Index Terms: speech recognition, precision grammar