Abstract
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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
