What Makes a Good Audio Prompt for Zero-Shot TTS? Learning Prompt Selection from Synthesis Outcomes

Zero-shot text-to-speech (TTS) typically conditions on a short speech prompt, which conveys timbre but provides only limited evidence of a speaker's prosodic patterns. We formulate Long-Prompt Zero-Shot TTS, a task that uses long speech prompts at inference time to reproduce speaker-specific prosody without target-speaker fine-tuning. We introduce LP-TTS-Bench, a bilingual benchmark of 37 speakers with 63.95 prompt hours and 2,728 held-out test utterances. Beyond intelligibility and timbre similarity, LP-TTS-Bench evaluates timing, prominence, intonation, and model-based prosody metrics, enabling multidimensional evaluation of prosody reproduction. As a reference system, we develop LPTTS, whose hierarchical Long-Prompt Encoder compresses long speech prompts into a fixed number of continuous prosody vectors. Experiments show improvements in several prosodic dimensions over a short-prompt baseline, with overall gains as prompt duration increases up to approximately 10 minutes and saturation at longer durations.

Recently, zero-shot text-to-speech (TTS) has enabled high-fidelity and expressive speech synthesis, but it often fails to imitate unseen speaking styles from uncommon scenarios (e.g., crosstalk, dialects). Moreover, fine-tuning pretrained models requires large, high-quality datasets, limiting rapid personalization. We propose VoiceTTA, a reinforcement learning-based test-time adaptation (TTA) method that improves voice imitation of pretrained zero-shot TTS models. VoiceTTA introduces two style rewards based on coefficient-of-variation differences of F0 and energy, combined with speaker similarity and intelligibility (WER from a pretrained Whisper model), and optimizes learnable prefixes via group relative preference optimization (GRPO) in a flow matching-based model at inference time. Extensive experiments demonstrate substantial improvements on uncommon speech prompts, outperforming state-of-the-art baselines. Audio samples are available at https://voicetta.pages.dev/

In recent years, flow-matching models have produced significant improvements in zero-shot text-to-speech synthesis. Conditioned on an audio prompt and text, these models learn a velocity field and generate speech by iteratively solving an ODE. During inference, the solver evolves a single state spanning both the prompt and the region to be generated, although only the generated region is ultimately retained. The discarded prompt state, however, still matters; its intermediate values influence generation through the velocity field that couples the two regions. As sampling continues, this state can drift away from the prescribed conditional path, introducing a discrepancy into subsequent generation updates. Unlike the unknown generated trajectory, the prompt path is available in closed form from the reference audio and the initial noise. We exploit this observation with Prompt-Consistency Inference (PCI), a training-free rule that restores the prompt block to its analytic value before each velocity evaluation, while leaving the generated block unchanged. PCI improves speaker similarity and intelligibility across the evaluated flow-matching TTS backbones without additional network evaluations. Our ablation studies further show that PCI keeps post-step prompt discrepancies smaller and that corrections covering the later sampling stages recover much of the observed similarity gain.