RLBR: Reinforcement Learning with Biasing Rewards for Contextual Speech Large Language Models
Abstract
Speech large language models (LLMs) have driven significant progress in end-to-end speech understanding and recognition, yet they continue to struggle with accurately recognizing rare words and domain-specific terminology. This paper presents a novel fine-tuning method, Reinforcement Learning with Biasing Rewards (RLBR), which employs a specialized biasing words preferred reward to explicitly emphasize biasing words in the reward calculation. In addition, we introduce reference-aware mechanisms that extend the reinforcement learning algorithm with reference transcription to strengthen the potential trajectory exploration space. Experiments on the LibriSpeech corpus across various biasing list sizes demonstrate that RLBR delivers substantial performance improvements over a strong supervised fine-tuning (SFT) baseline and consistently outperforms several recently published methods. The proposed approach achieves excellent performance on the LibriSpeech test-clean and test-other sets, reaching Biasing Word Error Rates (BWERs) of 0.59% / 2.11%, 1.09% / 3.24%, and 1.36% / 4.04% for biasing list sizes of 100, 500, and 1000, respectively, without compromising the overall WERs.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The paper presents RLBR, a novel fine-tuning approach for contextual biasing in speech large language models. The technical contribution is significant, with a well-defined methodology and promising experimental results that could impact the development of more accurate and context-aware speech recognition systems.
Methodology
The proposed RLBR method innovatively applies reinforcement learning to enhance contextual biasing in speech LLMs, focusing on rare and domain-specific terminology. The introduction of a specialized reward function that prioritizes biasing words is a significant advancement over traditional methods. The reference-aware mechanisms further strengthen the exploration space, which is a thoughtful addition that addresses limitations in existing RL applications in speech recognition. The methodology is well-structured, with clear definitions and justifications for each component, although it could benefit from more detailed explanations of the ablation studies.
Results & evaluation
The experiments conducted on the LibriSpeech corpus are robust, with comprehensive evaluations across various biasing list sizes. The reported results demonstrate substantial performance improvements over strong baselines, indicating the effectiveness of the RLBR approach. However, the paper could enhance its credibility by including comparisons with a wider range of existing methods and providing more detailed statistical analyses of the results.
Reproducibility
The implementation details are well-documented, including the architecture, training procedures, and parameter settings. However, the absence of a shared code repository or demo URL limits the reproducibility of the results. Providing access to the code would significantly enhance the paper's impact and facilitate further research in this area.
Limitations
While the paper presents a novel approach, it does not address potential limitations such as the scalability of the method to larger datasets or its performance in real-world applications outside the LibriSpeech corpus. Additionally, the reliance on specific architectures may limit the generalizability of the findings to other speech recognition systems.
Broader impact
The RLBR method has the potential to significantly improve speech recognition systems, particularly in applications requiring accurate transcription of rare or domain-specific terms. This advancement could enhance user experiences in various fields, including healthcare, legal, and technical domains, where precise terminology is crucial. The implications of this research extend to improving accessibility and usability of speech technologies for diverse user groups. The paper presents RLBR, a novel fine-tuning approach for contextual biasing in speech large language models. The technical contribution is significant, with a well-defined methodology and promising experimental results that could impact the development of more accurate and context-aware speech recognition systems.
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