TurboBias 2.0: Streaming Context-Biasing for Production-Efficient ASR Systems
Contextualization is essential for production automatic speech recognition (ASR) systems, where user-provided phrases must be recognized accurately under strict latency constraints. Although many context-biasing methods improve recognition accuracy, they often do not address the practical requirements of modern production ASR systems: streaming inference, efficient batched decoding, user-specific context lists, and low runtime overhead. We propose TurboBias 2.0, a production-oriented framework for efficient phrase boosting in Transducer-based ASR systems. The framework extends GPU-accelerated
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- FuzzySimilar title/name (fuzzy) · 59%openai/whisper-large-v3-turbo →
“Fuzzy title match (0.73): “TurboBias 2.0: Streaming Context-Biasing for Production-Effi” ≈ “openai/whisper-large-v3-turbo””
- FuzzySimilar title/name (fuzzy) · 59%Tongyi-MAI/Z-Image-Turbo →
“Fuzzy title match (0.73): “TurboBias 2.0: Streaming Context-Biasing for Production-Effi” ≈ “Tongyi-MAI/Z-Image-Turbo””
- PossiblePossibly related (embedding) · 57%Measuring benchmark optimization in speech recognition →
- PossiblePossibly related (embedding) · 50%Introducing the FFASR Leaderboard: Benchmarking ASR in the Real World →
- PossiblePossibly related (embedding) · 49%Launch HN: Speko (YC S26) – OpenRouter for Voice AI →
- FuzzySimilar title/name (fuzzy) · 59%drumih/turbo-fieldfare →
“Fuzzy title match (0.73): “TurboBias 2.0: Streaming Context-Biasing for Production-Effi” ≈ “drumih/turbo-fieldfare””
- LinkedLinked via arxiv author · 85%Vladimir Bataev →
“TurboBias 2.0: Streaming Context-Biasing for Production-Efficient ASR Systems”
- LinkedLinked via arxiv author · 85%Lilit Grigoryan →
“TurboBias 2.0: Streaming Context-Biasing for Production-Efficient ASR Systems”