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Speech LLMs are Contextual Reasoning Transcribers

arXiv:2604.00610v1 Announce Type: new Abstract: Despite extensions to speech inputs, effectively leveraging the rich knowledge and contextual understanding of large language models (LLMs) in automatic speech recognition (ASR) remains non-trivial, as the task primarily involves direct speech-to-text mapping. To address this, this paper proposes chain-of-thought ASR (CoT-ASR), which constructs a reasoning chain that enables LLMs to first analyze the input speech and generate contextual analysis, thereby fully exploiting their generative capabilities. With this contextual reasoning, CoT-ASR then performs more informed speech recognition and completes both reasoning and transcription in a single pass. Moreover, CoT-ASR naturally supports user-guided transcription: while designed to self-generate reasoning, it can also seamlessly incorporate user-provided context to guide transcription, further extending ASR functionality. To reduce the modality gap, this paper introduces a CTC-guided Moda

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Keqi Deng, Ruchao Fan, Bo Ren, Yiming Wang, Jinyu Li · · 1 min read · 2 views
#cs.CL

arXiv:2604.00610v1 Announce Type: new Abstract: Despite extensions to speech inputs, effectively leveraging the rich knowledge and contextual understanding of large language models (LLMs) in automatic speech recognition (ASR) remains non-trivial, as the task primarily involves direct speech-to-text mapping. To address this, this paper proposes chain-of-thought ASR (CoT-ASR), which constructs a reasoning chain that enables LLMs to first analyze the input speech and generate contextual analysis, thereby fully exploiting their generative capabilities. With this contextual reasoning, CoT-ASR then performs more informed speech recognition and completes both reasoning and transcription in a single pass. Moreover, CoT-ASR naturally supports user-guided transcription: while designed to self-generate reasoning, it can also seamlessly incorporate user-provided context to guide transcription, further extending ASR functionality. To reduce the modality gap, this paper introduces a CTC-guided Modality Adapter, which uses CTC non-blank token probabilities to weight LLM embeddings, efficiently aligning speech encoder outputs with the LLM's textual latent space. Experiments show that, compared to standard LLM-based ASR, CoT-ASR achieves a relative reduction of 8.7% in word error rate (WER) and 16.9% in entity error rate (EER).

Executive Summary

This paper proposes a novel approach to automatic speech recognition (ASR) by leveraging large language models (LLMs) in a more effective manner. Chain-of-thought ASR (CoT-ASR) constructs a reasoning chain that enables LLMs to analyze input speech, generate contextual analysis, and perform more informed speech recognition. The CTC-guided Modality Adapter is introduced to efficiently align speech encoder outputs with the LLM's textual latent space. Experiments show a significant reduction in word error rate (WER) and entity error rate (EER). The approach has the potential to improve the functionality and accuracy of ASR systems, particularly in scenarios where contextual understanding is crucial. However, further research is needed to fully explore the capabilities and limitations of this method.

Key Points

  • ▸ CoT-ASR constructs a reasoning chain to leverage LLMs' generative capabilities for ASR
  • ▸ The CTC-guided Modality Adapter aligns speech encoder outputs with the LLM's textual latent space
  • ▸ Experiments demonstrate a significant reduction in WER and EER

Merits

Strength in leveraging LLMs' generative capabilities

CoT-ASR enables LLMs to analyze input speech and generate contextual analysis, leading to more informed speech recognition. This approach effectively leverages the rich knowledge and contextual understanding of LLMs in ASR.

Demerits

Limitation in addressing potential modality gaps

While the CTC-guided Modality Adapter addresses the modality gap to some extent, further research is needed to ensure seamless alignment of speech encoder outputs with the LLM's textual latent space.

Expert Commentary

This paper showcases an innovative approach to ASR by harnessing the power of LLMs. The proposed CoT-ASR method demonstrates a promising reduction in WER and EER, highlighting the potential for significant improvements in ASR accuracy. However, the CTC-guided Modality Adapter, although effective in addressing the modality gap, may require further refinement to ensure optimal performance. The paper's emphasis on leveraging LLMs' generative capabilities is particularly noteworthy, as it opens up new avenues for research in multimodal learning and fusion. As the field continues to evolve, it will be essential to explore the limitations and potential applications of this approach.

Recommendations

  • ✓ Future research should focus on refining the CTC-guided Modality Adapter to ensure seamless alignment of speech encoder outputs with the LLM's textual latent space.
  • ✓ The proposed approach should be explored in various ASR applications, including voice assistants, transcription services, and human-computer interaction, to evaluate its practical implications.

Sources

Original: arXiv - cs.CL

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