arXiv:2606.18485v1 Announce Type: cross Abstract: Neural Text-to-Speech (TTS) systems achieve remarkable quality on short utterances but long-form speech generation shows prosodic drift, speaker inconsistencies and sentence boundary artifacts. Existing approaches either compress sequences, increase context length or naively concatenate independently synthesized chunks. We present an inference-time approach called MagpieTTS-LF that enables MagpieTTS to produce coherent long-form speech without model retraining. Our method introduces three key innovations: (1) soft attention priors to guide mono
The continuous improvement in neural TTS systems is leading researchers to address long-standing issues like long-form coherence without requiring extensive retraining, indicating a maturation of the field.
This development significantly enhances the practical utility of text-to-speech for extended content creation, reducing production complexities and costs for various applications.
Existing short-form TTS models can now be adapted for high-quality, long-form speech generation without expensive retraining, making sophisticated speech synthesis more accessible.
- · Content creators
- · Audiobook publishers
- · Podcasting platforms
- · AI voice providers
- · Traditional voice recording studios (for some applications)
- · Companies specializing in short-form TTS only
More natural and engaging synthetic long-form audio content becomes widely available.
The cost and time associated with producing audio versions of text-based content are substantially reduced, increasing overall audio content volume.
This could accelerate the development of autonomous AI agents capable of generating and consuming extensive audio-based information and narratives.
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Read at arXiv cs.AI