The K yoto Speech-to-Speech Translation System for IWSLT 2023 (2023)

Abstract: This paper describes the Kyoto speech-to-speech translation system for IWSLT 2023. Our system is a combination of speech-to-text translation and text-to-speech synthesis. For the speech-to-text translation model, we used the dual-decoderTransformer model. For text-to-speech synthesis model, we took a cascade approach of an acoustic model and a vocoder.

## introduction this paper describes the kyoto speech-to-speech translation system for iwslt 2023 @xcite . our system is a combination of speech-totext translation and text-to-speech synthesis. for speech-to-text translation model, we used dualdecoder transformer model following @xcite . for text-to-speech synthesis model, we took cascade approach of an acoustic model and a vocoder. we used fastspeech 2 @xcite as the acoustic model and hifi-gan @xcite as the vocoder. ## system description the speech-to-speech translation system is a combination of speech-to-text translation and text-tospeech synthesis. ## conclusion in this paper, we described our system, which is a combination of speech-to-text translation and textto-speech synthesis. for speech-to-text translation, we trained the dual-decoder transformer model with the gigast dataset filtered based on the similarity of multilingual sentence embeddings. for the text-to-speech synthesis model, we took a cascade approach of an acoustic model and a vocoder and used a combination of fastspeech 2 and hifi-gan. in the future, we will try to perform multi-level pretraining based on transforming speechut @xcite with phonemes as unit. we will also try to use encodec-based speech synthesis method similar to vall-ex @xcite to increase the accurate representation of emotions and vocal patterns.