This paper describes the NAIST submission to the English-to-German, Japanese, Chinese Simultaneous Speech-to-Text track at IWSLT 2025. Last year, our system was based on an end-to-end speech-to-text translation model that combined HuBERT and mBART. This year, the system consists of a Whisper encoder, the DeCo compressive projector, and the Qwen large language model. The simultaneous translation (SimulST) system is implemented by applying a local agreement policy to an offline-trained translation model. For the streaming translation (StreamST) system, we integrate an online version of the SHAS segmenter into our SimulST architecture. Our results demonstrate that adopting LLMs as the backbone architecture for speech translation tasks yields strong translation performance. Additionally, leveraging robust segmentation capability of SHAS for StreamST achieves good quality-latency trade-off when processing unbounded audio streams.

This paper presents NAIST’s submission to the offline speech translation task of the IWSLT 2025 evaluation campaign, focusing on English-to-German and English-to-Chinese translation. We implemented both cascade and end-to-end frameworks using various components. For the cascade approach, we used Whisper and SALMONN as automatic speech recognition systems, each paired with Qwen2.5 large language model (LLM) for translation. In the end-to-end setting, we used SALMONN as speech translation and also built a custom model combining the Whisper encoder, DeCo projector, and Qwen2.5 LLM. To further leverage the large language model capabilities, we experimented with different prompting strategies. Additionally, since long speech inputs are segmented for processing, we applied hypothesis combination techniques to generate the final translation output. Our results show that combining Whisper and LLMs can yield strong translation performance, even without further fine-tuning in the cascade setup. Moreover, our proposed end-to-end architecture achieved competitive results, despite being trained on significantly less data compared to SALMONN. Finally, we decided to use both SALMONN as an end-to-end speech translation model and our proposed end-to-end model for our IWSLT 2025 submission for both language pairs.

Despite being the world’s fourth-most populous country, the development of spoken language technologies in Indonesia still needs improvement. Most automatic speech recognition (ASR) systems that have been developed are still limited to transcribing the exact word-by-word, which, in many cases, consists of ambiguous sentences. In fact, speakers use prosodic characteristics of speech to convey different interpretations, which, unfortunately, these systems often ignore. In this study, we attempt to resolve structurally ambiguous utterances into unambiguous texts in Indonesian using prosodic information. To the best of our knowledge, this might be the first study to address this problem in the ASR context. Our contributions include (1) collecting the Indonesian speech corpus on structurally ambiguous sentences; (2) conducting a survey on how people disambiguate structurally ambiguous sentences presented in both text and speech forms; and (3) constructing an Indonesian ASR and meaning interpretation system by utilizing both cascade and direct approaches to map speech to text, along with two additional prosodic information signals (pause and pitch). The experimental results reveal that it is possible to disambiguate these utterances. In this study, the proposed cascade system, utilizing Mel-spectrograms concatenated with F0 and energy as input, achieved a disambiguation accuracy of 79.6%, while the proposed direct system with the same input yielded an even more impressive disambiguation accuracy of 82.2%.