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Kraken LLM advances speech-to-speech translation quality

By PulseAugur Editorial · [1 sources] ·

Researchers have developed Kraken, a novel speech-to-speech translation model that leverages LLMs and low-bitrate vector quantization for improved quality and non-linguistic information preservation. The model, built upon Qwen3-8B, uses a separate decoder, Autowave-X, conditioned on source speech to enhance speaker and prosody transfer. Kraken demonstrated superior translation quality compared to SeamlessM4T-Large v2 and Qwen2.5 Omni in evaluations using 150,000 hours of multilingual data. AI

IMPACT This research could lead to more natural and accurate speech-to-speech translation systems, improving cross-lingual communication.

RANK_REASON The cluster contains a research paper detailing a new model and its technical approach. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

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

Kraken LLM advances speech-to-speech translation quality

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The cluster contains a research paper detailing a new model and its technical approach. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Hayato Futami, Hassan Shahmohammadi, Tushar Dhyani, Alkis Koudounas, Rapha\"el Lafargue, Yosuke Kashiwagi, Quentin Jodelet, Emiru Tsunoo ·

    Kraken: LLM-based Speech-to-Speech Translation via Low-bitrate VQ and Dual-path Source Conditioning

    arXiv:2609.13045v1 Announce Type: new Abstract: Speech-to-speech translation (S2ST) has advanced significantly with speech LLMs, offering the potential for joint optimization and preserving non-linguistic information. However, these models struggle with predicting high-bitrate sp…

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