Streaming speech-to-text models often default to English when encountering multilingual audio due to a confidence issue rather than a language capability problem. This drift occurs because streaming models must make rapid decisions with limited audio context, leading to uncertainty. When unsure, these models revert to English, their most common training language, especially when faced with short utterances, code-switching, or noisy audio. AssemblyAI offers solutions, including a universal model that supports native code-switching across 18 languages, to address this drift and ensure accurate transcription of spoken languages. AI
IMPACT Provides insights for developers building multilingual voice products on improving transcription accuracy.
RANK_REASON Blog post explaining a technical issue and solution for a specific product type.
AI-generated summary · Google Gemini · from 1 sources. How we write summaries →
