Better Pseudo-labeling with Multi-ASR Fusion and Error Correction by SpeechLLM

• URL: http://arxiv.org/abs/2506.11089v1
• Date: Thu, 05 Jun 2025 12:35:53 GMT
• Title: Better Pseudo-labeling with Multi-ASR Fusion and Error Correction by SpeechLLM
• Authors: Jeena Prakash, Blessingh Kumar, Kadri Hacioglu, Bidisha Sharma, Sindhuja Gopalan, Malolan Chetlur, Shankar Venkatesan, Andreas Stolcke,
• Abstract summary: We propose a unified multi-ASR prompt-driven framework using postprocessing by either textual or speech-based large language models.<n>We show significant improvements in transcription accuracy compared to traditional methods.
• Score: 12.005825075325234
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automatic speech recognition (ASR) models rely on high-quality transcribed data for effective training. Generating pseudo-labels for large unlabeled audio datasets often relies on complex pipelines that combine multiple ASR outputs through multi-stage processing, leading to error propagation, information loss and disjoint optimization. We propose a unified multi-ASR prompt-driven framework using postprocessing by either textual or speech-based large language models (LLMs), replacing voting or other arbitration logic for reconciling the ensemble outputs. We perform a comparative study of multiple architectures with and without LLMs, showing significant improvements in transcription accuracy compared to traditional methods. Furthermore, we use the pseudo-labels generated by the various approaches to train semi-supervised ASR models for different datasets, again showing improved performance with textual and speechLLM transcriptions compared to baselines.

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