Enhancements in statistical spoken language translation by de-normalization of ASR results

11/18/2015 ∙ by Agnieszka Wołk, et al. ∙ 0

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.

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Code Repositories

Text-De-Normalizator

The application is used to de-normalize the results of automatic speech recognition (ASR) and text segmentation at sentence level in order to improve the quality of machine translation and S2S systems.


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