Enhancements in statistical spoken language translation by de-normalization of ASR results
This work addresses a domain-specific challenge for speech recognition and translation systems, with incremental improvements in processing Polish language data.
The research tackled the problem of improving spoken language translation by automatically segmenting sentences and de-normalizing ASR outputs in Polish, resulting in enhanced machine translation performance.
Spoken language translation (SLT) has become very important in an increasingly globalized world. Machine translation (MT) for automatic speech recognition (ASR) systems is a major challenge of great interest. This research investigates that automatic sentence segmentation of speech that is important for enriching speech recognition output and for aiding downstream language processing. This article focuses on the automatic sentence segmentation of speech and improving MT results. We explore the problem of identifying sentence boundaries in the transcriptions produced by automatic speech recognition systems in the Polish language. We also experiment with reverse normalization of the recognized speech samples.