  This paper describes NAIST's submission to the simultaneous track of the
IWSLT 2024 Evaluation Campaign: English-to-{German, Japanese, Chinese}
speech-to-text translation and English-to-Japanese speech-to-speech
translation. We develop a multilingual end-to-end speech-to-text translation
model combining two pre-trained language models, HuBERT and mBART. We trained
this model with two decoding policies, Local Agreement (LA) and AlignAtt. The
submitted models employ the LA policy because it outperformed the AlignAtt
policy in previous models. Our speech-to-speech translation method is a cascade
of the above speech-to-text model and an incremental text-to-speech (TTS)
module that incorporates a phoneme estimation model, a parallel acoustic model,
and a parallel WaveGAN vocoder. We improved our incremental TTS by applying the
Transformer architecture with the AlignAtt policy for the estimation model. The
results show that our upgraded TTS module contributed to improving the system
performance.
