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New SEA-HELM Audio benchmark reveals significant performance drop for voice AI

By PulseAugur Editorial · [1 sources] ·

A new research paper introduces SEA-HELM Audio, a framework designed to evaluate the performance of automatic speech recognition (ASR) and large language model (LLM) pipelines across multiple Southeast Asian languages. The study found significant performance degradation when using synthesized speech compared to text input, with accuracy drops ranging from 9 to over 42 percentage points depending on the task. Tamil-language performance showed the most substantial decline, highlighting the inadequacy of text-only benchmarks for predicting the reliability of voice-based AI systems. AI

IMPACT Highlights the critical need for speech-based evaluation to ensure reliable voice-AI systems, especially in multilingual contexts.

RANK_REASON Research paper introducing a new benchmark for evaluating ASR-LLM pipelines. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

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

New SEA-HELM Audio benchmark reveals significant performance drop for voice AI

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Research paper introducing a new benchmark for evaluating ASR-LLM pipelines. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Yixian Tan, Aulia Adila, Jonathan Heng, Kunat Pipatanakul, Donghang Wu, Anocha Sutaveephamochanon ·

    SEA-HELM Audio: Evaluating Multilingual ASR-LLM Pipeline Performance Across SEA-HELM Subsets

    arXiv:2610.19869v1 Announce Type: cross Abstract: We introduce SEA-HELM Audio, a Southeast Asian (SEA) multilingual evaluation framework that extends the text-based SEA-HELM benchmark to assess performance degradation in cascaded automatic speech recognition and large language mo…

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