Research
Benchmarking ASR on Code-Switched Speech
Hugging Face Bloghigh confidence
Why it matters
- →Code-switching is common among bilingual speakers, yet underrepresented in ASR research.
- →Accurate ASR is crucial for enterprise settings where transcription errors can have operational consequences.
- →The benchmark provides a valuable resource for improving ASR systems to better handle bilingual interactions.
Hugging Face has introduced a benchmark to assess the performance of automatic speech recognition (ASR) systems on code-switched speech, which is common among bilingual speakers. The benchmark evaluates models on transcription accuracy and semantic understanding across four language pairs. ElevenLabs Scribe V2 and Assembly AI Universal 3-Pro emerged as top performers in transcription accuracy, while Google Gemini 3 Flash excelled in semantic metrics. This initiative aims to improve the handling of code-switched speech in enterprise voice agents, addressing a significant gap in current ASR capabilities.
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