A Comprehensive Solution to Connect Speech Encoder and Large Language Model for ASR
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
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| _version_ | 1866910500258840576 |
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| author | Pham, Van Tung Lin, Yist Han, Tao Li, Wei Zhang, Jun Lu, Lu Wang, Yuxuan |
| author_facet | Pham, Van Tung Lin, Yist Han, Tao Li, Wei Zhang, Jun Lu, Lu Wang, Yuxuan |
| contents | Recent works have shown promising results in connecting speech encoders to large language models (LLMs) for speech recognition. However, several limitations persist, including limited fine-tuning options, a lack of mechanisms to enforce speech-text alignment, and high insertion errors especially in domain mismatch conditions. This paper presents a comprehensive solution to address these issues. We begin by investigating more thoughtful fine-tuning schemes. Next, we propose a matching loss to enhance alignment between modalities. Finally, we explore training and inference methods to mitigate high insertion errors. Experimental results on the Librispeech corpus demonstrate that partially fine-tuning the encoder and LLM using parameter-efficient methods, such as LoRA, is the most cost-effective approach. Additionally, the matching loss improves modality alignment, enhancing performance. The proposed training and inference methods significantly reduce insertion errors. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_17272 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Comprehensive Solution to Connect Speech Encoder and Large Language Model for ASR Pham, Van Tung Lin, Yist Han, Tao Li, Wei Zhang, Jun Lu, Lu Wang, Yuxuan Machine Learning Recent works have shown promising results in connecting speech encoders to large language models (LLMs) for speech recognition. However, several limitations persist, including limited fine-tuning options, a lack of mechanisms to enforce speech-text alignment, and high insertion errors especially in domain mismatch conditions. This paper presents a comprehensive solution to address these issues. We begin by investigating more thoughtful fine-tuning schemes. Next, we propose a matching loss to enhance alignment between modalities. Finally, we explore training and inference methods to mitigate high insertion errors. Experimental results on the Librispeech corpus demonstrate that partially fine-tuning the encoder and LLM using parameter-efficient methods, such as LoRA, is the most cost-effective approach. Additionally, the matching loss improves modality alignment, enhancing performance. The proposed training and inference methods significantly reduce insertion errors. |
| title | A Comprehensive Solution to Connect Speech Encoder and Large Language Model for ASR |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2406.17272 |