CMU ’s IWSLT 2025 Simultaneous Speech Translation System (2025)

Abstract: This paper presents CMU’s submission to the IWSLT 2025 Simultaneous Speech Translation (SST) task for translating unsegmented English speech into Chinese and German text in a streaming manner. Our end-to-end speech-to-text system integrates a chunkwise causal Wav2Vec 2.0 speech encoder, an adapter, and the Qwen2.5-7B-Instruct as the decoder. We use a two-stage simultaneous training procedure on robust speech segments synthesized from LibriSpeech, CommonVoice, and VoxPopuli datasets, utilizing standard cross-entropy loss. Our model supports adjustable latency through a configurable latency multiplier. Experimental results demonstrate that our system achieves 44.3 BLEU for English-to-Chinese and 25.1 BLEU for English-to-German translations on the ACL60/60 development set, with computation-aware latencies of 2.7 seconds and 2.3 seconds, and theoretical latencies of 2.2 and 1.7 seconds, respectively.

## introduction cmu's submission to the iwslt 2025 simultaneous speech-to-text translation track @xcite foot_0 is an end-to-end model that effectively translates unbounded english speech input into german and chinese text without speech segmentation. translating unbounded speech presents unique challenges. unlike segmented speech translation, it requires the model to maintain and process the speech and translation history so that translation quality, theoretical latency, and computation cost can be balanced @xcite . large language models (llms) have recently shown strong performance in improving speech translation quality @xcite @xcite @xcite , and modern llms now support long-context inference due to architectural and algorithmic advances @xcite . these two advantages were recently unified in infinisst @xcite , which frames simultaneous translation as a multiturn dialogue and enables inference on unbounded speech with minimal computational overhead. our system is built upon the infinisst framework and consists of: however, a major limitation in speech translation research is the scarcity of high-quality parallel speech-text data. only several hundred hours are available on resources such as europarl-st @xcite and covost2 @xcite . to scale infinisst training beyond this constraint, we synthesize training data by translating transcripts from automatic speech recognition (asr) datasets into target-language text using an llm. our experiments on the acl60/60 development set @xcite demonstrate that increasing the amount of synthesized speech translation data consistently improves translation quality, with gains observed even beyond 3,000 hours of training data. additionally, we find that qwen2.5-7b-instruct significantly outperforms llama3.1-8b-instruct @xcite on english-to-chinese translation and achieves comparable performance on english-to-german translation. the iwslt 2025 simultaneous speech-to-text translation track foot_1 focuses on translating unsegmented speech into target-language text using pretrained large language models (llms) and speech encoders. the evaluation data consists of unsegmented acl talks. for english-to-german, systems are additionally tested on accented speech, while a dedicated development set is provided for czech-to-english. systems are evaluated on both translation quality and latency. latency is measured using stream-laal @xcite , while translation quality is assessed using bleu @xcite and neural metrics such as comet @xcite , bleurt @xcite , etc. we participate in two language directions: english-to-chinese and english-to-german. for both directions, we submit models operating in the low-latency regime-achieving @xmath0.5 seconds for chinese on the development set. ## conclusion in this paper, we presented cmu's simultaneous speech translation system built upon the infinisst framework for the iwslt 2025 sst task. our end-to-end model employs a chunkwise causal wav2vec 2.0 encoder, a adapter, and the qwen2.5-7b-instruct decoder. we demonstrated that synthesizing training data by translating large-scale asr datasets significantly alleviates the limitations posed by limited parallel data, achieving substantial improvements in translation quality. our experiments indicated that the addition of synthesized data from voxpopuli provided consistent gains, and the qwen2.5-7b-instruct decoder notably outperformed alternatives, particularly in english-to-chinese translation. the proposed model effectively balances translation quality and computational latency, showcasing strong performance in a realistic, unbounded speech scenario.