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ASR systems benchmarked on code-switching speech

By PulseAugur Editorial · [1 sources] ·

A new benchmark study evaluated five commercial automatic speech recognition (ASR) systems on code-switching speech, specifically focusing on Arabic, Persian, and German mixed with English. The research introduced a novel pipeline using GPT-4o and Gemini 1.5 Pro to score transcripts, reducing LLM costs by 91% and employing BERTScore as a more reliable metric than traditional Word Error Rate (WER) for certain language pairs. ElevenLabs Scribe v2 emerged as the top performer, achieving the lowest WER and highest BERTScore across all tested language pairs. AI

IMPACT This research highlights the challenges in ASR for code-switching and introduces a more robust evaluation method, potentially guiding future development of multilingual speech technologies.

RANK_REASON The cluster contains an academic paper detailing a new benchmark and evaluation methodology for ASR systems. [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 →

ASR systems benchmarked on code-switching speech

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The cluster contains an academic paper detailing a new benchmark and evaluation methodology for ASR systems. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Sajjad Abdoli, Ghassan Al-Sumaidaee, Clayton W. Taylor, Ahmad ElShiekh, Ahmed Rashad ·

    Benchmarking Commercial ASR Systems on Code-Switching Speech: Arabic, Persian, and German

    arXiv:2605.19069v2 Announce Type: replace Abstract: Code-switching -- the natural alternation between two languages within a single utterance -- represents one of the most challenging and under-studied conditions for automatic speech recognition (ASR). Existing commercial ASR ben…

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