Direct speech-to-speech translation with a sequence-to-sequence model
Direct speech-to-speech translation with a sequence-to-sequence model

    
    
                
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            Abstract:We present an attention-based sequence-to-sequence neural network which can directly translate speech from one language into speech in another language, without relying on an intermediate text representation. The network is trained end-to-end, learning to map speech spectrograms into target spectrograms in another language, corresponding to the translated content (in a different canonical voice). We further demonstrate the ability to synthesize translated speech using the voice of the source speaker. We conduct experiments on two Spanish-to-English speech translation datasets, and find that the proposed model slightly underperforms a baseline cascade of a direct speech-to-text translation model and a text-to-speech synthesis model, demonstrating the feasibility of the approach on this very challenging task.
    

    
    
  
      Submission history From: Ye Jia [view email]                  [v1]
        Fri, 12 Apr 2019 05:15:31 UTC (228 KB)
    [v2]
        Tue, 25 Jun 2019 21:34:10 UTC (228 KB)
