想了解如何提升ASR系统准确率的同时保持高效？试试TurboBias 2.0，它为多用户个性化上下文偏置提供了新方案。
TurboBias 2.0是一个针对生产级自动语音识别系统的框架，通过流式推理和批处理解码，实现个性化上下文偏置，提高识别准确率，同时保持低延迟和高吞吐量。
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 TurboBias with a case-insensitive boosting graph and per-stream batched decoding, allowing each utterance in a batch to use an independent context-biasing configuration. This enables personalized context biasing for multiple simultaneous users without sharing or mixing their context lists. The proposed framework supports both offline and streaming inference and can be used with greedy and beam-search decoding. Experiments show that TurboBias 2.0 improves contextual phrase recognition while preserving low latency and high throughput.