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MaLa-ASR: Multimedia-Assisted LLM-Based ASR
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As more and more information-rich data like video become available, utilizing multi-modal auxiliary information to enhance audio tasks has sparked widespread research interest. The recent surge in research on LLM-based audio models provides fresh perspectives for tackling audio tasks. Given that LLM can flexibly ingest multiple inputs, we propose MaLa-ASR, an LLM-based ASR model that can integrate textual keywords extracted from presentation slides to improve recognition of conference content. MaLa-ASR yields average WERs of 9.4% and 11.7% on the L95 and S95 subsets of the SlideSpeech corpus, representing a significant relative WER drop of 27.9% and 44.7% over the baseline model reported in SlideSpeech. MaLa-ASR underscores LLM's strong performance in speech tasks and the capability to integrate auxiliary information conveniently. By adding keywords to the input prompt, the biased word error rate (B-WER) reduces relatively by 46.0% and 44.2%, establishing a new SOTA on this dataset.
Forward citations
Cited by 1 Pith paper
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Exploring Cross-Utterance Speech Contexts for Conformer-Transducer Speech Recognition Systems
Adding cross-utterance audio context to Conformer-Transducer ASR reduces WER/CER by 0.5 to 1.1 absolute points on four benchmarks, and a splicing-based batch scheme cuts training time by up to about 19%.
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