@inproceedings{hussain-etal-2020-german,
    title = "{G}erman-{A}rabic Speech-to-Speech Translation for Psychiatric Diagnosis",
    author = {Hussain, Juan  and
      Mediani, Mohammed  and
      Behr, Moritz  and
      Cheragui, M. Amin  and
      St{\"u}ker, Sebastian  and
      Waibel, Alexander},
    editor = "Zitouni, Imed  and
      Abdul-Mageed, Muhammad  and
      Bouamor, Houda  and
      Bougares, Fethi  and
      El-Haj, Mahmoud  and
      Tomeh, Nadi  and
      Zaghouani, Wajdi",
    booktitle = "Proceedings of the Fifth Arabic Natural Language Processing Workshop",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.wanlp-1.1",
    pages = "1--11",
    abstract = "In this paper we present the natural language processing components of our German-Arabic speech-to-speech translation system which is being deployed in the context of interpretation during psychiatric, diagnostic interviews. For this purpose we have built a pipe-lined speech-to-speech translation system consisting of automatic speech recognition, text post-processing/segmentation, machine translation and speech synthesis systems. We have implemented two pipe-lines, from German to Arabic and Arabic to German, in order to be able to conduct interpreted two-way dialogues between psychiatrists and potential patients. All systems in our pipeline have been realized as all-neural end-to-end systems, using different architectures suitable for the different components. The speech recognition systems use an encoder/decoder + attention architecture, the text segmentation component and the machine translation system are based on the Transformer architecture, and for the speech synthesis systems we use Tacotron 2 for generating spectrograms and WaveGlow as vocoder. The speech translation is deployed in a server-based speech translation application that implements a turn based translation between a German speaking psychiatrist administrating the Mini-International Neuropsychiatric Interview (M.I.N.I.) and an Arabic speaking person answering the interview. As this is a very specific domain, in addition to the linguistic challenges posed by translating between Arabic and German, we also focus in this paper on the methods we implemented for adapting our speech translation system to the domain of this psychiatric interview.",
}