Real-Time Statistical Speech Translation
This work addresses real-time voice communication between foreigners, but it is incremental as it applies existing statistical methods to speech translation with specific data preparations.
This research investigated statistical machine translation approaches for real-time speech translation using TED, Europarl, and OPUS parallel text corpora, evaluating data preparation techniques like part-of-speech tagging and compound splitting with BLEU, NIST, METEOR, and TER metrics to determine the most suitable metric for the PL-EN language pair.
This research investigates the Statistical Machine Translation approaches to translate speech in real time automatically. Such systems can be used in a pipeline with speech recognition and synthesis software in order to produce a real-time voice communication system between foreigners. We obtained three main data sets from spoken proceedings that represent three different types of human speech. TED, Europarl, and OPUS parallel text corpora were used as the basis for training of language models, for developmental tuning and testing of the translation system. We also conducted experiments involving part of speech tagging, compound splitting, linear language model interpolation, TrueCasing and morphosyntactic analysis. We evaluated the effects of variety of data preparations on the translation results using the BLEU, NIST, METEOR and TER metrics and tried to give answer which metric is most suitable for PL-EN language pair.