Conversational telephone speech recognition

This paper describes the development of a speech recognition system for the processing of telephone conversations, starting with a state-of-the-art broadcast news transcription system. We identify major changes and improvements in acoustic and language modeling, as well as decoding, which are required to achieve state-of-the-art performance on conversational speech. Some major changes on the acoustic side include the use of speaker normalization (VTLN), the need to cope with channel variability, and the need for efficient speaker adaptation and better pronunciation modeling. On the linguistic side the primary challenge is to cope with the limited amount of language model training data. To address this issue we make use of a data selection technique, and a smoothing technique based on a neural network language model. At the decoding level lattice rescoring and minimum word error decoding are applied. On the development data, the improvements yield an overall word error rate of 24.9% whereas the original BN transcription system had a word error rate of about 50% on the same data.