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.

The NTTSU team’s submission leverages several large language models developed through a training procedure that includes continual pre-training and supervised fine-tuning. For paragraph-level translation, we generated synthetic paragraph-aligned data and utilized this data for training.In the task of translating Japanese to Chinese, we particularly focused on the speech domain translation. Specifically, we built Whisper models for Japanese automatic speech recognition (ASR). We used YODAS dataset for Whisper training. Since this data contained many noisy data pairs, we combined the Whisper outputs using ROVER for polishing the transcriptions. Furthermore, to enhance the robustness of the translation model against errors in the transcriptions, we performed data augmentation by forward translation from audio, using both ASR and base translation models.To select the best translation from multiple hypotheses of the models, we applied Minimum Bayes Risk decoding + reranking, incorporating scores such as COMET-QE, COMET, and cosine similarity by LaBSE.

It remains a question that how simultaneous interpretation (SI) data affects simultaneous machine translation (SiMT). Research has been limited due to the lack of a large-scale training corpus. In this work, we aim to fill in the gap by introducing NAIST-SIC-Aligned, which is an automatically-aligned parallel English-Japanese SI dataset. Starting with a non-aligned corpus NAIST-SIC, we propose a two-stage alignment approach to make the corpus parallel and thus suitable for model training. The first stage is coarse alignment where we perform a many-to-many mapping between source and target sentences, and the second stage is fine-grained alignment where we perform intra- and inter-sentence filtering to improve the quality of aligned pairs. To ensure the quality of the corpus, each step has been validated either quantitatively or qualitatively. This is the first open-sourced large-scale parallel SI dataset in the literature. We also manually curated a small test set for evaluation purposes. Our results show that models trained with SI data lead to significant improvement in translation quality and latency over baselines. We hope our work advances research on SI corpora construction and SiMT. Our data will be released upon the paper’s acceptance.

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.

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.

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.

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.

Recent studies argue that knowledge distillation is promising for speech translation (ST) using end-to-end models. In this work, we investigate the effect of knowledge distillation with a cascade ST using automatic speech recognition (ASR) and machine translation (MT) models. We distill knowledge from a teacher model based on human transcripts to a student model based on erroneous transcriptions. Our experimental results demonstrated that knowledge distillation is beneficial for a cascade ST. Further investigation that combined knowledge distillation and fine-tuning revealed that the combination consistently improved two language pairs: English-Italian and Spanish-English.

This paper describes NAIST’s NMT system submitted to the IWSLT 2020 conversational speech translation task. We focus on the translation disfluent speech transcripts that include ASR errors and non-grammatical utterances. We tried a domain adaptation method by transferring the styles of out-of-domain data (United Nations Parallel Corpus) to be like in-domain data (Fisher transcripts). Our system results showed that the NMT model with domain adaptation outperformed a baseline. In addition, slight improvement by the style transfer was observed.