Abstract
In this paper, the application of LVCSR (Large Vocabulary Continuous Speech Recognition) technology is investigated for real-time, resource-limited broadcast close captioning. The work focuses on transcribing live broadcast conversation speech to make such programs accessible to deaf viewers. Due to computational limitations, real time factor (RTF) and memory requirements are kept low during decoding with various models tailored for Hungarian broadcast speech recognition. Two decoders are compared on the direct transcription task of broadcast conversation recordings, and setups employing re-speakers are also tested. Moreover, the models are evaluated on a broadcast news transcription task as well, and different language models (LMs) are tested in order to demonstrate the performance of our systems in settings when low memory consumption is a less crucial factor.
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Acknowledgement
This research has been partially funded by the PIAC_13-1-2013-0234 (Patimedia) and KMR_12-1-2012-0207 (DIANA) projects. The authors would also like to thank MTVA for their support towards this work.
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Varga, Á. et al. (2015). Automatic Close Captioning for Live Hungarian Television Broadcast Speech: A Fast and Resource-Efficient Approach. In: Ronzhin, A., Potapova, R., Fakotakis, N. (eds) Speech and Computer. SPECOM 2015. Lecture Notes in Computer Science(), vol 9319. Springer, Cham. https://doi.org/10.1007/978-3-319-23132-7_13
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DOI: https://doi.org/10.1007/978-3-319-23132-7_13
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