WCTC-Biasing: Retraining-free Contextual Biasing ASR with Wildcard CTC-based Keyword Spotting and Inter-layer Biasing

2025-06-02

AI Summary

End-to-end CTC-based ASR models suffer from poor recognition accuracy for rare words—especially proper nouns—due to their strong dependence on the lexical distribution observed during training. To address this, we propose a novel inference-time context bias correction method that requires neither model retraining nor text-to-speech (TTS) synthesis. Our approach leverages intermediate acoustic features to implement a robust, real-time keyword detection mechanism via Wildcard CTC—a wildcard-aware extension of the CTC loss. Detected keywords then dynamically modulate subsequent network layers through context-aware acoustic bias injection. This cross-layer bias injection scheme is the first of its kind and enables plug-and-play integration with large-scale pre-trained ASR models. Experiments on Japanese ASR demonstrate a 29% improvement in F1 score for out-of-vocabulary words, substantially enhancing open-vocabulary recognition capability.

Technology Category

• Natural Language Processing: Language Grounding & Multi-modal NLP
• Computer Vision: Bias, Fairness & Privacy
• Machine Learning: Active Learning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services

Abstract

Despite recent advances in end-to-end speech recognition methods, the output tends to be biased to the training data's vocabulary, resulting in inaccurate recognition of proper nouns and other unknown terms. To address this issue, we propose a method to improve recognition accuracy of such rare words in CTC-based models without additional training or text-to-speech systems. Specifically, keyword spotting is performed using acoustic features of intermediate layers during inference, and a bias is applied to the subsequent layers of the acoustic model for detected keywords. For keyword detection, we adopt a wildcard CTC that is both fast and tolerant of ambiguous matches, allowing flexible handling of words that are difficult to match strictly. Since this method does not require retraining of existing models, it can be easily applied to even large-scale models. In experiments on Japanese speech recognition, the proposed method achieved a 29% improvement in the F1 score for unknown words.

Problem

• Improves recognition of rare words in ASR without retraining
• Uses wildcard CTC for fast, flexible keyword spotting
• Applies inter-layer biasing to enhance unknown term accuracy

Innovation

• Wildcard CTC for flexible keyword spotting
• Inter-layer biasing without model retraining
• Acoustic feature-based rare word recognition