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SpeechLLMs show mixed results in translation benchmarks, cascades still lead

By PulseAugur Editorial · [1 sources] ·

A new comprehensive test suite, Hearing to Translate, has been developed to evaluate the effectiveness of integrating speech modality directly into Large Language Models (LLMs) for speech-to-text translation. The study benchmarks six state-of-the-art SpeechLLMs against sixteen cascaded systems, analyzing performance across 16 benchmarks, 13 language pairs, and 9 challenging conditions. Findings indicate that while cascaded systems remain the most reliable overall, recent SpeechLLMs can match or surpass them in specific scenarios, whereas standalone Speech Foundation Models (SFMs) generally lag behind. AI

IMPACT New benchmarks for SpeechLLMs may accelerate research into more efficient and accurate speech translation systems.

RANK_REASON This is a research paper introducing a new benchmark suite for evaluating SpeechLLMs.

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

SpeechLLMs show mixed results in translation benchmarks, cascades still lead

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This is a research paper introducing a new benchmark suite for evaluating SpeechLLMs.
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Sara Papi, Javier Garcia Gilabert, Zachary Hopton, Vil\'em Zouhar, Carlos Escolano, Gerard I. G\'allego, Jorge Iranzo-S\'anchez, Ahrii Kim, Dominik Mach\'a\v{c}ek, Patricia Schmidtova, Maike Z\"ufle ·

    Hearing to Translate: The Effectiveness of Speech Modality Integration into LLMs

    arXiv:2512.16378v4 Announce Type: replace Abstract: As Large Language Models (LLMs) expand beyond text, integrating speech as a native modality has given rise to SpeechLLMs, which directly process spoken language and enable speech-to-text translation (ST) and other downstream tas…

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