End-to-end Contrastive Language-Speech Pretraining Model For Long-form Spoken Question Answering
Abstract
Significant progress has been made in spoken question answering (SQA) in recent years. However, many existing methods, including large audio language models, struggle with processing long audio. Follow the success of retrieval augmented generation, a speech-related retriever shows promising in help preprocessing long-form speech. But the performance of existing speech-related retrievers is lacking. To address this challenge, we propose CLSR, an end-to-end contrastive language-speech retriever that efficiently extracts question-relevant segments from long audio recordings for downstream SQA task. Unlike conventional speech-text contrastive models, CLSR incorporates an intermediate step that converts acoustic features into text-like representations prior to alignment, thereby more effectively bridging the gap between modalities. Experimental results across four cross-modal retrieval datasets demonstrate that CLSR surpasses both end-to-end speech related retrievers and pipeline approaches combining speech recognition with text retrieval, providing a robust foundation for advancing practical long-form SQA applications.
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 CLSR, a novel end-to-end contrastive language-speech retriever that significantly enhances spoken question answering by effectively bridging acoustic and textual modalities. The comprehensive methodology and strong experimental results position this work as a valuable contribution to the field of audio processing and machine learning.
Methodology
The methodology presented in this paper introduces the CLSR model, which innovatively bridges the gap between acoustic and textual representations through an intermediate text-like representation. This approach addresses the limitations of existing models that struggle with long-form audio by leveraging a continuous integrate-and-fire mechanism and a vector quantizer to enhance the alignment between modalities. The model's architecture is well-structured, and the use of contrastive learning principles is effectively integrated into the retrieval process. However, the paper could benefit from a more detailed explanation of the CIF and sampler mechanisms, as well as their specific contributions to the overall performance improvements.
Results & evaluation
The experimental evaluation is robust, utilizing four diverse datasets to demonstrate the effectiveness of CLSR against both end-to-end and pipeline models. The results indicate significant improvements in retrieval performance, particularly in long-form spoken question answering tasks. The comparison with baseline models is thorough, and the ablation studies provide insights into the contributions of various components of the model. However, the paper could improve by including more extensive analysis of the results, such as error analysis or qualitative assessments of the retrieval outputs.
Reproducibility
The paper provides sufficient details regarding the experimental setup, including the datasets used, model configurations, and training procedures. The inclusion of a GitHub repository with the code enhances reproducibility. However, the lack of specific hyperparameter tuning details and the absence of a clear description of the computational resources used may hinder full reproducibility.
Limitations
One limitation of the study is the reliance on specific datasets that may not fully represent the diversity of spoken language contexts encountered in real-world applications. Additionally, while the CLSR model shows promising results, it may still face challenges in handling highly noisy audio inputs or very diverse accents, which are common in practical scenarios. The paper also does not address the scalability of the model when applied to larger datasets or more complex retrieval tasks.
Broader impact
The proposed CLSR model has significant implications for practical applications in spoken question answering systems, particularly in contexts such as education, customer service, and information retrieval from long audio sources. By improving the efficiency and accuracy of retrieving relevant audio segments, CLSR could enhance user experiences in various interactive voice applications. The model's architecture could also inspire future research in cross-modal learning and retrieval systems. The paper presents CLSR, a novel end-to-end contrastive language-speech retriever that significantly enhances spoken question answering by effectively bridging acoustic and textual modalities. The comprehensive methodology and strong experimental results position this work as a valuable contribution to the field of audio processing and machine learning.
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