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This method integrates the LMs directly into the parameters of an LLM-based ASR model, requiring no additional computational cost at inference, and consistently improved the ASR performance in the target domains, without degrading inference speed or memory footprint.
Speech-to-speech translation (S2ST) has advanced significantly with speech LLMs, offering the potential for joint optimization and preserving non-linguistic information. However, these models struggle with predicting high-bitrate speech tokens in LLMs, and face the challenge of relying on S2ST training data with ideall...
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