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New model uses continuous space for speech recognition and translation

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced ELF-S2T, a novel approach to speech-to-text systems that operates in a continuous latent space rather than discrete text tokens. This model, built on the Embedded Language Flows (ELF) backbone, uses audio conditioning and flow-matching denoising for both speech recognition and translation tasks. Experiments on standard datasets demonstrate competitive performance and reveal that errors in both recognition and translation stem from similar confusions within this continuous latent space. AI

IMPACT This research suggests a unified approach to speech recognition and translation by leveraging continuous latent spaces, potentially simplifying future model development.

RANK_REASON The cluster contains a research paper detailing a new model architecture and experimental results. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New model uses continuous space for speech recognition and translation

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The cluster contains a research paper detailing a new model architecture and experimental results. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Xuanchen Li, Tianrui Wang, Yuheng Lu, Zikang Huang, Yu Jiang, Chenghan Lin, Chenrui Cui, Ziyang Ma, Xingyu Ma, Chunyu Qiang, Guochen Yu, Xie Chen, Longbiao Wang, Jianwu Dang ·

    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 …

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