Summary
TurboBias 2.0 is a production-oriented framework for efficient phrase boosting in Transducer-based automatic speech recognition (ASR) systems, presented by Vladimir Bataev, Lilit Grigoryan, Andrei Andrusenko, Nikolay Karpov, Vitaly Lavrukhin, and Boris Ginsburg (arXiv:2608.21343). Contextualization is essential for production ASR, where user-provided phrases must be recognized accurately under strict latency constraints. Many existing context-biasing methods improve accuracy but ignore practical production needs: streaming inference, efficient batched decoding, user-specific context lists, and low runtime overhead. TurboBias 2.0 extends GPU-accelerated TurboBias with a case-insensitive boosting graph and per-stream batched decoding, so each utterance in a batch can use an independent context-biasing configuration. This enables personalized context biasing for multiple simultaneous users without sharing or mixing their context lists. The framework supports both offline and streaming inference and works with greedy and beam search decoding. Experiments show that TurboBias 2.0 improves contextual phrase recognition while maintaining low latency and high throughput.
Paper Overview
Research Area: NLP
Authors: Vladimir Bataev, Lilit Grigoryan, Andrei Andrusenko, Nikolay Karpov, Vitaly Lavrukhin, Boris Ginsburg
Published: 2026-08-21
arXiv: 2608.21343
Original Abstract
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 demonstrate that TurboBias 2.0 improves contextual phrase recognition while maintaining low latency and high throughput.
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