Dynamic language modeling for European Portuguese
This paper reports on the work done on vocabulary and language model daily adaptation for a European Portuguese broadcast news transcription system. The proposed adaptation framework takes into consideration European Portuguese language characteristics, such as its high level of inflection and complex verbal system. A multi-pass speech recognition framework using contemporary written texts available daily on the Web is proposed. It uses morpho-syntactic knowledge (part-of-speech information) about an in-domain training corpus for daily selection of an optimal vocabulary. Using an information retrieval engine and the ASR hypotheses as query material, relevant documents are extracted from a dynamic and large-size dataset to generate a story-based language model. When applied to a daily and live closed-captioning system of live TV broadcasts, it was shown to be effective, with a relative reduction of out-of-vocabulary word rate (69%) and WER (12.0%) when compared to the results obtained by the baseline system with the same vocabulary size. (C) 2010 Elsevier Ltd. All rights reserved.
language modeling broadcast news transcription vocabulary selection information retrieval techniques automatic speech recognition (asr)