LibriVoxDeEn: A Corpus for German-to-English Speech Translation and German Speech Recognition

Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020), pages 3590–3594 Marseille, 11–16 May 2020 c⃝ European Language Resources Association (ELRA), licensed under CC-BY-NC 3590 LibriVoxDeEn: A Corpus for German-to-English Speech Translation and German Speech Recognition Benjamin Beilharz∗, Xin Sun∗, Sariya Karimova∗, Stefan Riezler†,∗ ∗Computational Linguistics & †IWR Heidelberg University, Germany {beilharz,karimova,riezler}@cl.uni-heidelberg.de, xin.sun@stud.uni-heidelberg.de Abstract We present a corpus of sentence-aligned triples of German audio, German text, and English translation, based on German audio books. The speech translation data consist of 110 hours of audio material aligned to over 50k parallel sentences. An even larger dataset comprising 547 hours of German speech aligned to German text is available for speech recognition. The audio data is read speech and thus low in disfluencies. The quality of audio and sentence alignments has been checked by a manual evaluation, showing that speech alignment quality is in general very high. The sentence alignment quality is comparable to well-used parallel translation data and can be adjusted by cutoffs on the automatic alignment score. To our knowledge, this corpus is to date the largest resource for German speech recognition and for end-to-end German-to-English speech translation. Keywords: Spoken Language Translation, Speech Recognition, German Audiobooks 1. Introduction Direct speech translation has recently been shown to be feasible using a single sequence-to-sequence neural model, trained on parallel data consisting of source audio, source text and target text. The crucial advantage of such end-to- end approaches is the avoidance of error propagation as in a pipeline approaches of speech recognition and text transla- tion. While cascaded approaches have an advantage in that they can straightforwardly use large independent datasets for speech recognition and text translation, clever sharing of sub-networks via multi-task learning and two-stage model- ing (Weiss et al., 2017; Anastasopoulos and Chiang, 2018; Sperber et al., 2019) has closed the performance gap be- tween end-to-end and pipeline approaches. However, end- to-end neural speech translation is very data hungry while available datasets must be considered large if they exceed 100 hours of audio. For example, the widely used Fisher and Callhome Spanish-English corpus (Post et al., 2013) comprises 162 hours of audio and 138, 819 parallel sen- tences. Larger corpora for end-to-end speech translation have only recently become available for speech translation from English sources. For example, 236 hours of audio and 131, 395 parallel sentences are available for English-French speech translation based on audio books (Kocabiyikoglu et al., 2018; Bérard et al., 2018). For speech translation of English TED talks, 400-500 hours of audio aligned to around 250, 000 parallel sentences depending on the lan- guage pair have been provided for eight target languages by Di Gangi et al. (2019). Pure speech recognition data are available in amounts of 1, 000 hours of read English speech and their transcriptions in the LibriSpeech corpus provided by Panayotov et al. (2015). When it comes to German sources, the situation regard- ing corpora for end-to-end speech translation as well as for speech recognition is dire. To our knowledge, the largest …

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