Zero-Shot Text-to-Speech (ZS-TTS)

• Zero-Shot Text-to-Speech is a paradigm that synthesizes speech for unseen speakers using minimal reference data with advanced control over style, prosody, and accent.
• It leverages techniques such as acoustic tokenization, multi-scale conditioning, and latent space manipulation to enable cross-lingual voice cloning and rapid adaptation.
• The approach addresses data scarcity with transfer learning, pseudo-labeling, diffusion models, and efficient inference techniques to optimize synthesis quality.

Zero-Shot Text-to-Speech (ZS-TTS) is a research paradigm and collection of neural techniques designed to synthesize speech in the voice of an unseen speaker from minimal reference data—typically without any supervised training on that speaker. Leveraging advances in representation learning, acoustic modeling, and conditioning strategies, state-of-the-art ZS-TTS systems enable high-fidelity voice synthesis, rapid speaker adaptation, and cross-lingual voice cloning, and now support flexible style, prosody, and attribute control for both research and application domains.

1. Core Techniques and Modeling Principles

ZS-TTS systems share a set of foundational architectural and methodological principles:

• Acoustic Tokenization and Disentanglement: Most ZS-TTS models transform speech into discrete, factorized token representations corresponding to phonetic content, prosody, timbre, and other speaker or style factors. For instance, OZSpeech utilizes a neural codec (“FACodec”) to extract six token streams, each representing distinct speech characteristics such as prosody, content, and acoustic details (Huynh-Nguyen et al., 19 May 2025). This explicit disentanglement is critical for accurate attribute transfer in zero-shot settings.
• Conditioning on Reference Prompts: All state-of-the-art methods use at least one reference prompt—a short audio segment, phoneme sequence, or, in newer work, multi-modal prompt (speech, text, or image)—from the target speaker or attribute (Wu et al., 24 May 2025). Embedding networks (e.g., speaker encoders, style encoders, emotion encoders) extract conditioning vectors, which are fused into the acoustic model at various stages.
• Hierarchical and Multi-Scale Representations: The need for fine control over style and voice has led to multi-scale conditioning strategies, e.g., StyleTTS-ZS models prosody with time-varying vectors rather than relying only on global embeddings (Li et al., 16 Sep 2024), and multi-sentence style prompts can be fused at both phoneme and frame levels (Lei et al., 2023).
• Latent Space Manipulation and Data Augmentation: Data scarcity and lack of supervised examples are addressed with approaches such as latent filling (in the speaker embedding space) (Bae et al., 2023), pseudo-labeling from self-supervised speech representations (Kim et al., 2022), and cross-modality alignment between embedding streams (Tang et al., 2021).
• Sequence Alignment and Regulator Modules: Accurate duration prediction and upsampling are pivotal; many methods employ specialized duration predictors or average upsampling to align text and speech features (ensuring seamless inpainting or speech extension) (Tang et al., 2021, Zhu et al., 16 Jun 2025).
• Diffusion, Flow-Matching, and Distillation: Recent advances replace autoregressive or GAN decoders with flow-matching ODEs (Huynh-Nguyen et al., 19 May 2025, Zhu et al., 16 Jun 2025), diffusion models with classifier-free guidance (Li et al., 16 Sep 2024), and their accelerated variants via student-teacher distillation to achieve one-step or very low-NFE (number of function evaluations) inference.
• Adversarial and Consistency Losses: Both objective (L2/L1 on spectrograms, MSE on embeddings) and perceptual/adversarial losses are used to drive naturalness and disentangled representation learning; latent consistency losses encourage that augmented embeddings yield outputs matching the intended speaker identity (Bae et al., 2023).

2. Data-Efficient Transfer and Cross-Lingual/Multilingual Extensions

ZS-TTS methods are architected to generalize from very limited or even zero supervised data for novel speakers or languages:

• Transfer Learning With Unlabeled Data: Pre-training on large-scale, unlabeled speech corpora provides generalized acoustic encoders and robust feature extractors (e.g., wav2vec2.0, HuBERT) (Kim et al., 2022, Fujita et al., 2023). Pseudo phonemes via unsupervised clustering allow models to learn rudimentary speech-to-text alignments even without ground-truth transcripts.
• Multilingual Masked LM Pretraining: Unsupervised text-only pretraining on massive multilingual data followed by partial freezing of a language-aware embedding layer allows TTS models to perform zero-shot synthesis in languages without text–audio pairs (Saeki et al., 2023). This enables support for hundreds or thousands of languages.
• Dialect and Accent Control: Fine-grained pseudo-labeling, as in XTTS finetuning for Arabic dialects (Doan et al., 24 Jun 2024), and the learning of explicit accent embeddings that are disentangled from speaker identity (Zhong et al., 13 Sep 2024) empower models to generate geographically or sociolinguistically specific pronunciations and prosodies.
• Prompt-Based Style/Emotion Modulation: Recent systems, e.g., StyleFusion-TTS, support zero-shot and fully multi-modal reference-based control, combining speaker and style reference samples (text, audio, image), and enforce disentanglement via adversarial objectives and hierarchical fusion (Chen et al., 24 Sep 2024, Wu et al., 24 May 2025).

3. Model Architectures and Training Frameworks

Below is a comparative table summarizing the principal architectures and their salient elements:

Model	Core Architecture	Conditioning/Alignment
OZSpeech (Huynh-Nguyen et al., 19 May 2025)	Codec+Prior Generator+FM (1-step)	Factorized speech tokens, learned prior
XTTS (Casanova et al., 7 Jun 2024)	VQ-VAE+GPT2+HiFi-GAN	32 conditioning embeddings, Perceiver
StyleTTS-ZS (Li et al., 16 Sep 2024)	Prosody AE+Time-varying Diffusion+Distill	Time-varying style codes, classifier-free guidance
ZipVoice (Zhu et al., 16 Jun 2025)	Zipformer encoder+FM+Distill	Average upsampling alignment
MultiVerse (Bak et al., 4 Oct 2024)	Source-filter transformers+dual prosody	AR/non-AR prosody, FiLM prompt mod.
StyleFusion-TTS (Chen et al., 24 Sep 2024)	Flow-VAE+Hier. Conformer, GSF-enc	Multi-modal prompt, disentangled style/speaker
Intelli-Z (Jung et al., 25 Jan 2024)	Conformer backbone+KD+cycle-consistency	Selective aggregation using aperiodicity mask

In most cases, model variants adopt a factorized or modular approach, making it possible to scale to new types of control (accent, emotion, rhythm) and to accelerate inference without quality loss. Efficiency gains—either through codebook filtering, token compression, flow distillation, or one-step ODEs—are central to models such as XTTS and ZipVoice.

4. Evaluation Metrics and Empirical Performance

ZS-TTS evaluation requires considerations of synthesis naturalness, speaker similarity, attribute transfer fidelity, intelligibility, prosody, and efficiency. Common metrics across the literature include:

• Mean Opinion Score (MOS): Both for naturalness and speaker similarity (SMOS, DMOS).
• Word Error Rate (WER), Character Error Rate (CER): Assessed via ASR models on TTS outputs (e.g., WhisperX).
• Speaker Encoder Cosine Similarity (SECS): Assesses match between embeddings of generated and reference speech.
• Pitch/Energy Correlation, Prosody Similarity: Quantified via F₀ Pearson correlation, mean absolute error, or RMSE.
• Latency and Model Size: Reported as Number of Function Evaluations (NFE), real-time factor (RTF), or parameter counts.
• Novelty Metrics: For privacy/unlearning applications, new metrics such as speaker-Zero Retrain Forgetting (spk-ZRF) are proposed (Kim et al., 27 Jul 2025) to measure the randomness or effectiveness of identity removal.

Strong empirical results are claimed across all recent directions. For example, StyleTTS-ZS achieves MOS for naturalness and similarity exceeding or matching prior SOTA models, with a 10–20× reduction in inference time (Li et al., 16 Sep 2024). OZSpeech reaches markedly low WER (down to 0.05) in zero-shot synthesis with sub-30% model size of competing architectures (Huynh-Nguyen et al., 19 May 2025). XTTS achieves robust performance in 16 languages, with fast fine-tuning enabling cross-dialect synthesis (Casanova et al., 7 Jun 2024, Doan et al., 24 Jun 2024).

Notably, in multi-task and multi-modal scenarios, ablation studies confirm that model modularity, latent disentanglement, and fusion/filling techniques are each critical contributors to quality and control.

5. Applications, Limitations, and Emerging Directions

ZS-TTS methodologies underpin applications spanning:

• Voice Editing, Replacement, and Inpainting: Seamless speech editing in narration and audiobook pipelines (context-aware one-stage frameworks) (Tang et al., 2021).
• Personalization and Accessibility: Voice cloning and adaptive assistance in dialogue systems, including custom emotion or accent rendering (Wu et al., 24 May 2025, Zhong et al., 13 Sep 2024).
• Language and Dialect Revitalization: Deployment in low-resource and endangered language contexts, leveraging only text data or minimal paired audio (Saeki et al., 2023, Casanova et al., 7 Jun 2024).
• Pronunciation Assessment: Golden speech synthesis for computer-assisted pronunciation training (CAPT) and evaluation in L2 learning (Lo et al., 11 Sep 2024).
• Data Augmentation and Speaker Verification: Enhancing short-utterance speaker verification via embeddings derived from synthetic speech (Zhao et al., 17 Jun 2025).

There are, however, limitations and open challenges:

• Privacy and Ethical Concerns: The capacity to clone voices from short prompts raises regulatory and consent issues. Research such as speaker identity unlearning employs machine unlearning frameworks (e.g., teacher-guided unlearning with randomness incorporation) to neutralize a model’s ability to reproduce forbidden voices (Kim et al., 27 Jul 2025).
• Accent/Attribute Disentanglement: While several approaches such as GenAID and adversarial learning improve accent fidelity and controllability, perfect separation of speaker, style, and accent remains nontrivial, especially under limited or noisy data (Zhong et al., 13 Sep 2024).
• Cross-Lingual Artifacting and Domain Shift: Despite advances, WER and MOS frequently degrade under heavy domain shifts (e.g., dialect or low-resource language adaptation) (Doan et al., 24 Jun 2024, Bak et al., 4 Oct 2024). A plausible implication is that further gains will require more robust representations and hybrid supervised–unsupervised strategies.
• Latency vs. Quality Trade-Offs: Even with efficient diffusion distillation and flow-matching, there remains a subtle trade-off between reducing inference steps (NFE) and maximizing prosody/style fidelity.

6. Future Outlook and Research Directions

Trends in ZS-TTS research indicate sustained focus on:

• Expanding Expressive and Multi-Modal Control: The integration of emotion/image/text prompts (Wu et al., 24 May 2025, Chen et al., 24 Sep 2024), richer prosody modeling (Bak et al., 4 Oct 2024), and control over long-form context and style memory (Lei et al., 2023).
• Privacy-Preserving Synthesis and Selective Unlearning: Research into speaker identity “unlearning” frameworks, discrimination-aware modeling, and adversarial defenses (Kim et al., 27 Jul 2025).
• Efficient, Data-Light Generalization: Techniques such as optimal transport flow matching in a learned prior latent space (Huynh-Nguyen et al., 19 May 2025), and sample-efficient few-shot and zero-shot transfer using self-supervised and proxy-labeled data (Kim et al., 2022).
• Continuous and Streaming TTS: Architectures explicitly designed for infinite text streams and chunk-level generation with semantic guidance (Dang et al., 1 Oct 2024).
• Further Modularization and Disentanglement: To allow independent manipulation and control over content, prosody, timbre, accent, and emotion, enabling more flexible downstream applications and user customization.

7. Representative Table of Recent ZS-TTS Advances

System	Factorization	Control Modalities	Notable Innovations
OZSpeech	Discrete tokens	Speaker, prosody, text	One-step sampling, learned prior/factorization
StyleTTS-ZS	Diffusion+RVQ	Prosody (fine/time-varying)	Single-step distillation, adversarial MM loss
XTTS	VQ-VAE+GPT-2	32-embedding conditioning	Language/dialect extension, codebook filtering
MPE-TTS	Hier. encoding	Text/image/speech emotion	Multimodal prompt emotion, emotion consistency loss
MultiVerse	Source-filter model	Cross-lingual, prosody	AR/non-AR prosody fusion, FiLM prompt modulation

• “Zero-Shot Text-to-Speech for Text-Based Insertion in Audio Narration” (Tang et al., 2021)
• “Transfer Learning Framework for Low-Resource Text-to-Speech using a Large-Scale Unlabeled Speech Corpus” (Kim et al., 2022)
• “Learning to Speak from Text: Zero-Shot Multilingual Text-to-Speech with Unsupervised Text Pretraining” (Saeki et al., 2023)
• “Zero-shot text-to-speech synthesis conditioned using self-supervised speech representation model” (Fujita et al., 2023)
• “Improving LLM-Based Zero-Shot Text-to-Speech Synthesis with Multi-Scale Acoustic Prompts” (Lei et al., 2023)
• “Latent Filling: Latent Space Data Augmentation for Zero-shot Speech Synthesis” (Bae et al., 2023)
• “Intelli-Z: Toward Intelligible Zero-Shot TTS” (Jung et al., 25 Jan 2024)
• “XTTS: a Massively Multilingual Zero-Shot Text-to-Speech Model” (Casanova et al., 7 Jun 2024)
• “Towards Zero-Shot Text-To-Speech for Arabic Dialects” (Doan et al., 24 Jun 2024)
• “Zero-Shot Text-to-Speech as Golden Speech Generator: A Systematic Framework and its Applicability in Automatic Pronunciation Assessment” (Lo et al., 11 Sep 2024)
• “AccentBox: Towards High-Fidelity Zero-Shot Accent Generation” (Zhong et al., 13 Sep 2024)
• “StyleTTS-ZS: Efficient High-Quality Zero-Shot Text-to-Speech Synthesis with Distilled Time-Varying Style Diffusion” (Li et al., 16 Sep 2024)
• “StyleFusion TTS: Multimodal Style-control and Enhanced Feature Fusion for Zero-shot Text-to-speech Synthesis” (Chen et al., 24 Sep 2024)
• “Zero-Shot Text-to-Speech from Continuous Text Streams” (Dang et al., 1 Oct 2024)
• “MultiVerse: Efficient and Expressive Zero-Shot Multi-Task Text-to-Speech” (Bak et al., 4 Oct 2024)
• “OZSpeech: One-step Zero-shot Speech Synthesis with Learned-Prior-Conditioned Flow Matching” (Huynh-Nguyen et al., 19 May 2025)
• “MPE-TTS: Customized Emotion Zero-Shot Text-To-Speech Using Multi-Modal Prompt” (Wu et al., 24 May 2025)
• “ZipVoice: Fast and High-Quality Zero-Shot Text-to-Speech with Flow Matching” (Zhu et al., 16 Jun 2025)
• “Investigation of Zero-shot Text-to-Speech Models for Enhancing Short-Utterance Speaker Verification” (Zhao et al., 17 Jun 2025)
• “Do Not Mimic My Voice: Speaker Identity Unlearning for Zero-Shot Text-to-Speech” (Kim et al., 27 Jul 2025)