Adapting Speech Foundation Models with Large Language Models for Unified Speech Recognition
Abstract
Unified speech recognition aims to perform auditory, visual, and audiovisual speech recognition within a single model framework. While speech foundation models (SFMs) have demonstrated remarkable performance in auditory tasks, their adaptation to multimodal scenarios remains underexplored. This paper presents UASR-LLM, a novel framework that adapts frozen SFMs to unified VSR, ASR, and AVSR tasks by leveraging large language models (LLMs) as text decoders. Our approach introduces visual representations into multiple SFM layers through visual injection modules, enabling multimodal input processing and unified hidden representations. The augmented SFMs connect with decoder-only LLMs via a feed-forward adaptor, where concatenated representations and instruction prompts guide speech transcription. We implement a twostage training strategy: visual injection pretraining followed by speech recognition finetuning. SFM parameters remain frozen throughout training, with only visual injection modules optimized initially, and LLMs finetuned using LoRA parameters subsequently. Experimental results demonstrate superior performance over state-of-the-art baselines across VSR, ASR, and AVSR tasks under both clean and noisy conditions. Ablation studies confirm generalization across various SFMs and LLMs, validating the proposed training strategy.
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 main contribution of this paper is the introduction of UASR-LLM, a unified framework that effectively integrates visual and auditory modalities for improved speech recognition across multiple tasks. This work advances the field by demonstrating how existing models can be adapted to enhance performance in challenging conditions, thus paving the way for future research in multimodal speech recognition systems.
Methodology
The proposed UASR-LLM framework presents a novel approach to unified speech recognition by integrating visual representations into speech foundation models (SFMs) through visual injection modules. The two-stage training strategy, which includes visual injection pretraining followed by speech recognition finetuning, is well-structured and innovative. The use of large language models (LLMs) as decoders is a significant methodological advancement, allowing for improved transcription across multiple modalities. The incorporation of cross-modal knowledge distillation and the careful design of the visual injection modules demonstrate a thoughtful approach to leveraging existing models while enhancing their capabilities.
Results & evaluation
The experimental results are robust, showcasing superior performance across VSR, ASR, and AVSR tasks compared to state-of-the-art baselines. The paper provides comprehensive evaluations under both clean and noisy conditions, which is crucial for real-world applicability. The ablation studies further validate the effectiveness of the proposed training strategy and the generalization capabilities of the framework across different SFMs and LLMs. However, the paper could benefit from more detailed comparisons with additional baselines to strengthen its claims.
Reproducibility
The paper includes a thorough description of the datasets, model configurations, and training strategies, which aids in reproducibility. However, the absence of publicly available code or a project URL limits the ability for other researchers to replicate the experiments fully. Including a link to a code repository would significantly enhance reproducibility.
Limitations
The paper acknowledges several limitations, including the computational burden associated with LLM integration and the reliance on large-scale audio-visual datasets for optimal performance. The authors also note that while their method improves recognition robustness, it still lags behind previous lipreading studies that utilize larger datasets, indicating a need for further exploration of visual encoder capabilities.
Broader impact
The proposed framework has significant implications for practical applications in various domains, such as intelligent home systems, online conferencing, and service robotics, where robust speech recognition is essential. The integration of visual information enhances the system's performance in noisy environments, making it particularly valuable for real-world scenarios. The main contribution of this paper is the introduction of UASR-LLM, a unified framework that effectively integrates visual and auditory modalities for improved speech recognition across multiple tasks. This work advances the field by demonstrating how existing models can be adapted to enhance performance in challenging conditions, thus paving the way for future research in multimodal speech recognition systems.
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