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Joint Audio and Speech Understanding
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Humans are surrounded by audio signals that include both speech and non-speech sounds. The recognition and understanding of speech and non-speech audio events, along with a profound comprehension of the relationship between them, constitute fundamental cognitive capabilities. For the first time, we build a machine learning model, called LTU-AS, that has a conceptually similar universal audio perception and advanced reasoning ability. Specifically, by integrating Whisper as a perception module and LLaMA as a reasoning module, LTU-AS can simultaneously recognize and jointly understand spoken text, speech paralinguistics, and non-speech audio events - almost everything perceivable from audio signals.
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Cited by 1 Pith paper
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Unlocking Speech Instruction Data Potential with Query Rewriting
A multi-LLM rewriting and multi-agent validation pipeline makes text-to-speech synthesized speech instruction data far more usable and improves downstream speech instruction following.
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