Researchers have developed a new framework called TurboBias 2.0 for improving the accuracy of automatic speech recognition (ASR) systems in production environments. The system addresses several practical challenges, including streaming inference, efficient decoding, and user-specific context lists, while maintaining low latency and high throughput. According to the authors, their approach can be used with both greedy and beam-search decoding methods. Experiments show that TurboBias 2.0 improves contextual phrase recognition compared to other methods. --- Why it matters: This research matters for engineers working on ASR systems because it provides a more efficient and accurate way to recognize user-provided phrases in production environments, which is crucial for applications such as voice assistants and customer service chatbots. Source: https://arxiv.org/abs/2608.21343