Yuka Ko


2023

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NAIST Simultaneous Speech-to-speech Translation System for IWSLT 2023
Ryo Fukuda | Yuta Nishikawa | Yasumasa Kano | Yuka Ko | Tomoya Yanagita | Kosuke Doi | Mana Makinae | Sakriani Sakti | Katsuhito Sudoh | Satoshi Nakamura
Proceedings of the 20th International Conference on Spoken Language Translation (IWSLT 2023)

This paper describes NAIST’s submission to the IWSLT 2023 Simultaneous Speech Translation task: English-to-German, Japanese, Chinese speech-to-text translation and English-to-Japanese speech-to-speech translation. Our speech-to-text system uses an end-to-end multilingual speech translation model based on large-scale pre-trained speech and text models. We add Inter-connections into the model to incorporate the outputs from intermediate layers of the pre-trained speech model and augment prefix-to-prefix text data using Bilingual Prefix Alignment to enhance the simultaneity of the offline speech translation model. Our speech-to-speech system employs an incremental text-to-speech module that consists of a Japanese pronunciation estimation model, an acoustic model, and a neural vocoder.

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Tagged End-to-End Simultaneous Speech Translation Training Using Simultaneous Interpretation Data
Yuka Ko | Ryo Fukuda | Yuta Nishikawa | Yasumasa Kano | Katsuhito Sudoh | Satoshi Nakamura
Proceedings of the 20th International Conference on Spoken Language Translation (IWSLT 2023)

Simultaneous speech translation (SimulST) translates partial speech inputs incrementally. Although the monotonic correspondence between input and output is preferable for smaller latency, it is not the case for distant language pairs such as English and Japanese. A prospective approach to this problem is to mimic simultaneous interpretation (SI) using SI data to train a SimulST model. However, the size of such SI data is limited, so the SI data should be used together with ordinary bilingual data whose translations are given in offline. In this paper, we propose an effective way to train a SimulST model using mixed data of SI and offline. The proposed method trains a single model using the mixed data with style tags that tell the model to generate SI- or offline-style outputs. Experiment results show improvements of BLEURT in different latency ranges, and our analyses revealed the proposed model generates SI-style outputs more than the baseline.

2022

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NAIST Simultaneous Speech-to-Text Translation System for IWSLT 2022
Ryo Fukuda | Yuka Ko | Yasumasa Kano | Kosuke Doi | Hirotaka Tokuyama | Sakriani Sakti | Katsuhito Sudoh | Satoshi Nakamura
Proceedings of the 19th International Conference on Spoken Language Translation (IWSLT 2022)

This paper describes NAIST’s simultaneous speech translation systems developed for IWSLT 2022 Evaluation Campaign. We participated the speech-to-speech track for English-to-German and English-to-Japanese. Our primary submissions were end-to-end systems using adaptive segmentation policies based on Prefix Alignment.

2021

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NAIST English-to-Japanese Simultaneous Translation System for IWSLT 2021 Simultaneous Text-to-text Task
Ryo Fukuda | Yui Oka | Yasumasa Kano | Yuki Yano | Yuka Ko | Hirotaka Tokuyama | Kosuke Doi | Sakriani Sakti | Katsuhito Sudoh | Satoshi Nakamura
Proceedings of the 18th International Conference on Spoken Language Translation (IWSLT 2021)

This paper describes NAIST’s system for the English-to-Japanese Simultaneous Text-to-text Translation Task in IWSLT 2021 Evaluation Campaign. Our primary submission is based on wait-k neural machine translation with sequence-level knowledge distillation to encourage literal translation.