Speech Meets ELF: Audio Conditional Continuous-Target Diffusion for Speech Recognition and Translation
📰 ArXiv cs.AI
Learn how to improve speech recognition and translation using audio conditional continuous-target diffusion with ELF-S2T
Action Steps
- Build an ELF-S2T model using the pre-trained Embedded Language Flows backbone
- Apply audio conditional continuous-target diffusion to generate continuous text tokens
- Configure the model for speech recognition and translation tasks
- Test the performance of ELF-S2T on benchmark datasets
- Compare the results with traditional discrete-text token generation methods
Who Needs to Know This
Researchers and engineers working on speech-to-text systems can benefit from this approach to enhance the accuracy of automatic speech recognition and translation
Key Insight
💡 ELF-S2T's continuous-target generative model can enhance speech-to-text systems
Share This
💡 Improve speech recognition & translation with ELF-S2T's audio conditional continuous-target diffusion!
Key Takeaways
Learn how to improve speech recognition and translation using audio conditional continuous-target diffusion with ELF-S2T
Full Article
Title: Speech Meets ELF: Audio Conditional Continuous-Target Diffusion for Speech Recognition and Translation
Abstract:
arXiv:2606.10368v1 Announce Type: cross Abstract: Speech-to-text (S2T) systems for recognition (ASR) and translation (S2TT) typically generate discrete text tokens. In contrast, continuous-target language modelling performs generation in a continuous space, yet its potential for S2T remains unexplored. To bridge this gap, we propose ELF-S2T, an audio-conditioned continuous-target generative model for S2T. Built upon the pre-trained Embedded Language Flows (ELF) backbone, ELF-S2T processes speech
Abstract:
arXiv:2606.10368v1 Announce Type: cross Abstract: Speech-to-text (S2T) systems for recognition (ASR) and translation (S2TT) typically generate discrete text tokens. In contrast, continuous-target language modelling performs generation in a continuous space, yet its potential for S2T remains unexplored. To bridge this gap, we propose ELF-S2T, an audio-conditioned continuous-target generative model for S2T. Built upon the pre-trained Embedded Language Flows (ELF) backbone, ELF-S2T processes speech
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