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MELD speech model optimizes encoder and language model jointly

By PulseAugur Editorial · [2 sources] ·

Researchers have developed MELD, a novel speech language modeling approach that utilizes discrete latent variables on mel spectrograms. This method jointly optimizes the encoder and the speech language model, addressing limitations of separately optimized encoders. MELD demonstrates improvements over existing baselines in zero-shot Text-to-Speech and Speech-to-Text tasks, while also mitigating common issues like prolonged silence and word omissions in autoregressive mel-spectrogram modeling. AI

IMPACT This joint optimization approach could lead to more robust and efficient speech synthesis and recognition systems.

RANK_REASON The cluster contains an academic paper detailing a new model and its methodology.

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

MELD speech model optimizes encoder and language model jointly

COVERAGE [2]

  1. arXiv cs.CL TIER_1 English(EN) · Sung-Lin Yeh, Wei Zhou, Gil Keren, Duc Le, Zhong Meng, Hao Tang, Jay Mahadeokar, Ozlem Kalinli, Alexandre Mourachko ·

    MELD: Mel-Spectrogram-Based Speech Language Modeling with Discrete Latent Variables

    arXiv:2605.29859v1 Announce Type: cross Abstract: Recent speech language models rely on encoders that are optimized separately from autoregressive models. Since these encoders are unaware of the downstream objectives, the extracted representations may not be optimal for downstrea…

  2. arXiv cs.CL TIER_1 English(EN) · Alexandre Mourachko ·

    MELD: Mel-Spectrogram-Based Speech Language Modeling with Discrete Latent Variables

    Recent speech language models rely on encoders that are optimized separately from autoregressive models. Since these encoders are unaware of the downstream objectives, the extracted representations may not be optimal for downstream tasks. To address this limitation, we introduce …

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