Meta-data conditional language modeling
Michiel Bacchiani, Brian Roark · 2004
Automatic speech recognition (ASR) often occurs in circumstances in which knowledge external to the speech signal, or meta-data, is given. For example, a company receiving a call from a customer might have access to a database record of that customer. Conditioning the ASR models directly on this information to improve the transcription accuracy is hampered because, generally, the meta-data takes on many values and a training corpus has little data for each meta-data condition. The paper presents an algorithm to construct language models conditioned on such metadata. It uses tree-based clustering of the the training data to derive automatically meta-data projections, useful as language model conditioning contexts. The algorithm was tested on a multiple domain voice mail transcription task. We compare the performance of an adapted system aware of the domain shift to a system that only has meta-data to infer that fact. The meta-data used were the caller ID strings associated with the voice mail messages. The meta-data adapted system matched the performance of the system adapted using the domain knowledge explicitly.