Transformer-Based Direct Speech-To-Speech Translation with Transcoder

Traditional speech translation systems use a cascade manner that concatenates speech recognition (ASR), machine translation (MT), and text-to-speech (TTS) synthesis to translate speech from one language to another language in a step-by-step manner. Unfortunately, since those components are trained separately, MT often struggles to handle ASR errors, resulting in unnatural translation results. Recently, one work attempted to construct direct speech translation in a single model. The model used a multi-task scheme that learns to predict not only the target speech spectrograms directly but also the source and target phoneme transcription as auxiliary tasks. However, that work was only evaluated Spanish-English language pairs with similar syntax and word order. With syntactically distant language pairs, speech translation requires distant word order, and thus direct speech frame-to-frame alignments become difficult. Another direction was to construct a single deep-learning framework while keeping the step-by-step translation process. However, such studies focused only on speech-to-text translation. Furthermore, all of these works were based on a recurrent neural net-work (RNN) model. In this work, we propose a step-by-step scheme to a complete end-to-end speech-to-speech translation and propose a Transformer-based speech translation using Transcoder. We compare our proposed and multi-task model using syntactically similar and distant language pairs.