LibriS2S: A German-English Speech-to-Speech Translation Corpus
This addresses the language barrier problem for speech-to-speech translation researchers by providing a new dataset, though it is incremental as it adapts existing models.
The authors tackled the lack of publicly available training data for speech-to-speech translation by creating LibriS2S, the first German-English speech-to-speech corpus, and used it to propose Text-to-Speech models that integrate source language information like pitch and energy.
Recently, we have seen an increasing interest in the area of speech-to-text translation. This has led to astonishing improvements in this area. In contrast, the activities in the area of speech-to-speech translation is still limited, although it is essential to overcome the language barrier. We believe that one of the limiting factors is the availability of appropriate training data. We address this issue by creating LibriS2S, to our knowledge the first publicly available speech-to-speech training corpus between German and English. For this corpus, we used independently created audio for German and English leading to an unbiased pronunciation of the text in both languages. This allows the creation of a new text-to-speech and speech-to-speech translation model that directly learns to generate the speech signal based on the pronunciation of the source language. Using this created corpus, we propose Text-to-Speech models based on the example of the recently proposed FastSpeech 2 model that integrates source language information. We do this by adapting the model to take information such as the pitch, energy or transcript from the source speech as additional input.