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This paper introduces a gradient-based speech-to-text alignment method applicable to any differentiable ASR model, including CTC, transducer, attention-based encoder-decoder, and speech large language models, requiring no training or model modification.
Proposes Gradient-Based Connections (GBC), a method that models multi-agent LLM systems as computational graphs and uses gradient signals to attribute errors to specific agents, enabling better system-level optimization.