Peter Polák

Also published as: Peter Polak


2021

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ELITR Multilingual Live Subtitling: Demo and Strategy
Ondřej Bojar | Dominik Macháček | Sangeet Sagar | Otakar Smrž | Jonáš Kratochvíl | Peter Polák | Ebrahim Ansari | Mohammad Mahmoudi | Rishu Kumar | Dario Franceschini | Chiara Canton | Ivan Simonini | Thai-Son Nguyen | Felix Schneider | Sebastian Stüker | Alex Waibel | Barry Haddow | Rico Sennrich | Philip Williams
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations

This paper presents an automatic speech translation system aimed at live subtitling of conference presentations. We describe the overall architecture and key processing components. More importantly, we explain our strategy for building a complex system for end-users from numerous individual components, each of which has been tested only in laboratory conditions. The system is a working prototype that is routinely tested in recognizing English, Czech, and German speech and presenting it translated simultaneously into 42 target languages.

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Explainable Quality Estimation: CUNI Eval4NLP Submission
Peter Polák | Muskaan Singh | Ondřej Bojar
Proceedings of the 2nd Workshop on Evaluation and Comparison of NLP Systems

2020

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CUNI Neural ASR with Phoneme-Level Intermediate Step for~Non-Native~SLT at IWSLT 2020
Peter Polák | Sangeet Sagar | Dominik Macháček | Ondřej Bojar
Proceedings of the 17th International Conference on Spoken Language Translation

In this paper, we present our submission to the Non-Native Speech Translation Task for IWSLT 2020. Our main contribution is a proposed speech recognition pipeline that consists of an acoustic model and a phoneme-to-grapheme model. As an intermediate representation, we utilize phonemes. We demonstrate that the proposed pipeline surpasses commercially used automatic speech recognition (ASR) and submit it into the ASR track. We complement this ASR with off-the-shelf MT systems to take part also in the speech translation track.

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Large Corpus of Czech Parliament Plenary Hearings
Jonas Kratochvil | Peter Polak | Ondrej Bojar
Proceedings of the 12th Language Resources and Evaluation Conference

We present a large corpus of Czech parliament plenary sessions. The corpus consists of approximately 1200 hours of speech data and corresponding text transcriptions. The whole corpus has been segmented to short audio segments making it suitable for both training and evaluation of automatic speech recognition (ASR) systems. The source language of the corpus is Czech, which makes it a valuable resource for future research as only a few public datasets are available in the Czech language. We complement the data release with experiments of two baseline ASR systems trained on the presented data: the more traditional approach implemented in the Kaldi ASRtoolkit which combines hidden Markov models and deep neural networks (NN) and a modern ASR architecture implemented in Jaspertoolkit which uses deep NNs in an end-to-end fashion.