Understanding Zero-shot Rare Word Recognition Improvements Through LLM Integration

• URL: http://arxiv.org/abs/2502.16142v1
• Date: Sat, 22 Feb 2025 08:30:38 GMT
• Title: Understanding Zero-shot Rare Word Recognition Improvements Through LLM Integration
• Authors: Haoxuan Wang,
• Abstract summary: We investigate the integration of a large language model (LLM) with an automatic speech recognition (ASR) system.<n>Our analysis reveals that the LLM contributes significantly to improvements in rare word error rate (R-WER)<n>Through extensive ablation studies, we highlight the importance of adapter integration in aligning speech encoder outputs with the LLM's linguistic capabilities.
• Score: 0.8702432681310401
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this study, we investigate the integration of a large language model (LLM) with an automatic speech recognition (ASR) system, specifically focusing on enhancing rare word recognition performance. Using a 190,000-hour dataset primarily sourced from YouTube, pre-processed with Whisper V3 pseudo-labeling, we demonstrate that the LLM-ASR architecture outperforms traditional Zipformer-Transducer models in the zero-shot rare word recognition task, after training on a large dataset. Our analysis reveals that the LLM contributes significantly to improvements in rare word error rate (R-WER), while the speech encoder primarily determines overall transcription performance (Orthographic Word Error Rate, O-WER, and Normalized Word Error Rate, N-WER). Through extensive ablation studies, we highlight the importance of adapter integration in aligning speech encoder outputs with the LLM's linguistic capabilities. Furthermore, we emphasize the critical role of high-quality labeled data in achieving optimal performance. These findings provide valuable insights into the synergy between LLM-based ASR architectures, paving the way for future advancements in large-scale LLM-based speech recognition systems.

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