CoAT adds a continuous latent thinking space to LALMs via expert distillation to retain acoustic information, yielding gains on audio reasoning, understanding, music, emotion, and transcription benchmarks across three models.
Gsqa: An end-to-end model for generative spoken question answering.arXiv preprint arXiv:2312.09781, 2023
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CLSR is an end-to-end contrastive language-speech retriever using an intermediate text-like conversion step to improve retrieval of relevant segments from long audio for spoken question answering.
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Continuous Audio Thinking for Large Audio Language Models
CoAT adds a continuous latent thinking space to LALMs via expert distillation to retain acoustic information, yielding gains on audio reasoning, understanding, music, emotion, and transcription benchmarks across three models.
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End-to-end Contrastive Language-Speech Pretraining Model For Long-form Spoken Question Answering
CLSR is an end-to-end contrastive language-speech retriever using an intermediate text-like conversion step to improve retrieval of relevant segments from long audio for spoken question answering.