Speech Meets ELF: Audio Conditional Continuous-Target Diffusion for Speech Recognition and Translation
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 via a frozen Whisper encoder and a single linear projector, prepending the resulting audio conditio
The continuous evolution of AI language models and speech processing technologies allows for new approaches to fundamental speech-to-text tasks, pushing beyond traditional discrete token generation.
This research introduces a novel, continuous-target approach to speech recognition and translation, potentially improving accuracy, robustness, and efficiency over current discrete text token systems.
The paradigm shifts from generating discrete text tokens to generating in a continuous space, which could lead to more nuanced and flexible speech processing systems in the future.
- · AI research institutions
- · Developers of speech AI applications
- · Companies with large audio datasets
- · Traditional discrete token ASR/S2TT systems (if continuous-target overtakes)
Improved accuracy and fluency in speech recognition and translation models, especially for nuanced or low-resource languages.
Reduced computational overhead for certain speech processing tasks due to continuous-target generation, leading to broader deployment.
New AI agent capabilities that rely on highly robust and nuanced understanding of spoken language, transforming human-computer interaction.
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Read at arXiv cs.AI