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deepmind.google·6d ago
AI Engineering
Hugging Face Blog·Jun 9·5 min read
Can Voice Agents Handle Bilingual Customers? Benchmarking Frontier ASR on Code-Switched Speech
TL;DR
Bilingual speakers often use code-switching, which poses challenges for voice agents in accurately transcribing speech. To address this, a benchmark dataset was created to evaluate automatic speech recognition (ASR) models on code-switched speech across four language pairs.
✦ Why It Matters
Engineers can leverage this benchmark to improve ASR systems for bilingual applications, enhancing user experience.
Key Takeaways
- Test your ASR systems against the new code-switching benchmark.
- Focus on models with low WER and AER for better performance.
- Consider the impact of language switches on transcription accuracy.
- Utilize findings to improve customer support for bilingual users.
How It Works
The benchmark was created using a dataset of code-switched utterances generated from parallel user interactions. A language model was employed to produce realistic code-switching, and audio was synthesized for evaluation.
The performance of various ASR models was then assessed using established metrics to quantify transcription accuracy and semantic understanding.