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arXiv · 2412.16846

KALL-E:Autoregressive Speech Synthesis with Next-Distribution Prediction

Kangxiang Xia·Xinfa Zhu·Jixun Yao·Wenjie Tian·Wenhao Li·Lei Xie
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Abstract

We introduce KALL-E, a novel autoregressive (AR) language model for text-to-speech (TTS) synthesis that operates by predicting the next distribution of continuous speech frames. Unlike existing methods, KALL-E directly models the continuous speech distribution conditioned on text, eliminating the need for any diffusion-based components. Specifically, we utilize a Flow-VAE to extract a continuous latent speech representation from waveforms, instead of relying on discrete speech tokens. A single AR Transformer is then trained to predict these continuous speech distributions from text, optimizing a Kullback-Leibler divergence loss as its objective. Experimental results demonstrate that KALL-E achieves superior speech synthesis quality and can even adapt to a target speaker from just a single sample. Importantly, KALL-E provides a more direct and effective approach for utilizing continuous speech representations in TTS.

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Kangxiang Xia, Xinfa Zhu, Jixun Yao, Wenjie Tian, Wenhao Li, Lei Xie. 2025-09-17. KALL-E:Autoregressive Speech Synthesis with Next-Distribution Prediction. https://arxiv.org/abs/2412.16846

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