Language model adaptation for automatic call transcription

This paper presents a method of language model adaptation for call-center conversations using automatic speech recognition (ASR) transcripts and their confidence scores. The goal is to select the optimal adaptation set by estimating the recognition errors and minimizing the adaptation language model (LM) perplexity. ASR transcripts are ranked with respect to their confidence scores and adaptation data selection is done iteratively by filtering the most reliable transcript set that minimizes the LM perplexity. Model adaptation is then carried out by interpolating the selected adaptation LM with the baseline in-domain LM. We have evaluated our approach on agent speech of real call-center conversations and experiments show that 4% relative word error rate reduction is achieved with the proposed approach.