SLURP
Emerging28papers using it
2022first seen
Papers using SLURP (28)
- Structured Pruning Of Self-supervised Pre-trained Models For Speech Recognition And UnderstandingNon-autoregressive End-to-end Approaches For Joint Automatic Speech Recognition And Spoken Language UnderstandingCorpus Synthesis For Zero-shot ASR Domain Adaptation Using Large Language ModelsIntegrating Pretrained ASR And LM To Perform Sequence Generation For Spoken Language UnderstandingQUADS: QUAntized Distillation Framework for Efficient Speech Language UnderstandingJoint Automatic Speech Recognition And Structure Learning For Better
Speech UnderstandingEffectiveness Of Text, Acoustic, And Lattice-based Representations In Spoken Language Understanding TasksJoint Automatic Speech Recognition And Structure Learning For Better Speech UnderstandingCIF-PT: Bridging Speech And Text Representations For Spoken Language Understanding Via Continuous Integrate-and-fire Pre-trainingTask Arithmetic Can Mitigate Synthetic-to-real Gap In Automatic Speech RecognitionStructured Pruning of Self-Supervised Pre-trained Models for Speech
Recognition and UnderstandingTwo-Pass Low Latency End-to-End Spoken Language UnderstandingNon-autoregressive End-to-end Approaches for Joint Automatic Speech
Recognition and Spoken Language UnderstandingEnd-to-end spoken language understanding using joint CTC loss and
self-supervised, pretrained acoustic encodersThe Interpreter Understands Your Meaning: End-to-end Spoken Language
Understanding Aided by Speech TranslationPersonalized Predictive ASR for Latency Reduction in Voice AssistantsCIF-PT: Bridging Speech and Text Representations for Spoken Language
Understanding via Continuous Integrate-and-Fire Pre-TrainingKnowledge-Aware Audio-Grounded Generative Slot Filling for Limited
Annotated DataLeveraging Pretrained ASR Encoders for Effective and Efficient
End-to-End Speech Intent Classification and Slot FillingIntegrating Pretrained ASR and LM to Perform Sequence Generation for
Spoken Language UnderstandingCorpus Synthesis for Zero-shot ASR domain Adaptation using Large
Language ModelsImproving End-to-End Speech Processing by Efficient Text Data
Utilization with Latent SynthesisGeneralized zero-shot audio-to-intent classification1SPU: 1-step Speech Processing UnitSpeech-based Slot Filling using Large Language ModelsTask Arithmetic can Mitigate Synthetic-to-Real Gap in Automatic Speech
RecognitionPrompting Whisper for QA-driven Zero-shot End-to-end Spoken Language
UnderstandingWHISMA: A Speech-LLM to Perform Zero-shot Spoken Language Understanding