SpeechSynth Synthetic Dataset Overview

• SpeechSynth Synthetic Dataset is a synthetic corpus created using state-of-the-art TTS, voice conversion, and diffusion models to simulate human speech for research.
• It supports diverse applications including speaker verification, deepfake detection, dialogue modeling, and low-resource language technology with comprehensive annotations.
• Key methodologies incorporate multi-stage synthesis pipelines, paralinguistic event insertion, post-processing augmentation, and rigorous evaluation metrics such as MOS, WER, and EER.

A synthetic speech dataset consists of audio corpora generated—wholly or in part—by computational speech synthesis or voice conversion systems, with the explicit aim of research, benchmarking, or validating machine learning-driven models in speech technology, forensics, or security. Such datasets are foundational for evaluating deep learning techniques in speaker verification, speech recognition, dialog systems, deepfake detection, and related domains.

1. Definition and Scope

Synthetic speech datasets comprise speech audio created via neural TTS (text-to-speech), voice conversion (VC), or other generative models, functioning as surrogates or complements to natural human recordings. Synthetic speech corpora can cover various tasks, including complete utterance generation, fine-grained partial-forgery (e.g., segment insertion or splicing), and paralinguistic event synthesis. Architectures utilized span conventional pipeline TTS, state-of-the-art GANs, diffusion models, flow-matching, and multi-stage pipelines incorporating voice cloning and multilingual capabilities. Datasets may be monolingual, bilingual, or span dozens of languages; they may target the simulation of dialog, command-and-control, broadcast/narrative speech, or courtroom/forensic scenarios.

2. Dataset Construction Methodologies

Generation of a synthetic speech dataset typically follows a multi-stage pipeline:

• Text Selection and Scripting: Source text is drawn from established corpora (e.g., MultiWOZ for dialog (Lee et al., 2023), LibriSpeech, or custom prompts designed via LLMs (DeRenzi et al., 23 Jul 2025)), or derived from transcription of real datasets (e.g., VidTIMIT or authentic VoxCeleb2 audio (Miao et al., 2023)). Advanced pipelines use LLMs to generate structured dialogue metadata, controlling for scenario, role, emotion, and paralinguistics (Wang et al., 31 Mar 2025).
• TTS and Voice Conversion Architectures: Synthesis utilizes models such as FastSpeech2 + HiFi-GAN (Lee et al., 2023), GlowTTS, VITS, BigVGAN, TorToiSe (for zero-shot voice cloning), XTTS-v2/YourTTS for cross-lingual or resource-poor languages (DeRenzi et al., 23 Jul 2025), orthogonal Householder transform anonymization (Miao et al., 2023), and recent diffusion-based models (e.g., GradTTS, ProDiff, Openvoice2, Wavegrad2, UnitSpeech) (Bhagtani et al., 2024). Speaker embeddings may be derived from ECAPA-TDNN, x-vectors, or trained speaker encoders.
• Paralinguistic and Conversational Variability: Recent frameworks support inserting non-lexical events (laughter, sighs, gasps, backchannels) and realistic conversational overlaps, via segment-level annotation and automated taxonomy tagging (Bai et al., 18 Sep 2025, Zhou et al., 4 Sep 2025). Paralinguistic tokens can be injected at precise timestamps, or the TTS engine may be guided by high-level script annotations (Wang et al., 31 Mar 2025).
• Augmentation and Post-processing: Synthetic utterances are post-processed for realism, with techniques such as room impulse response convolution, additive noise (e.g., MUSAN, OpenSLR 28), background extraction, or dynamic time warping for A/V alignment (Salvi et al., 2022, Miao et al., 2023). Some pipelines incorporate quality filtering via ASR-based verification and SNR-based best-sample scoring (Gan et al., 11 Nov 2025).
• Annotation and Metadata: Datasets are annotated with generator IDs (for attributions), speaker IDs, language/command labels, scenario tags, and detailed paralinguistic markers, supporting both monomodal and multimodal evaluation (Wang et al., 31 Mar 2025, Huang et al., 29 Jul 2025).

3. Dataset Structure and Content

The structural properties of synthetic speech datasets are highly variable, but common features include:

• Size/Scale: From hundreds (speaker-specific testbeds (Deroy, 15 Jan 2026)) to millions (state-of-the-art forensics/deepfake corpora (Huang et al., 29 Jul 2025)) of utterances, with durations from <1 hour (e.g., legal scenarios) to several thousand hours (multilingual coverage; SpeechFake: >3,000 h (Huang et al., 29 Jul 2025); African ASR: ~2,900 h (DeRenzi et al., 23 Jul 2025)).
• Speaker Diversity: Can range from single-speaker (LJ Speech, TIMIT-TTS (Salvi et al., 2022)) to thousands (VoxCeleb1/2-based: 7,245 speakers (Gan et al., 11 Nov 2025); CLEAR-Global: 720 speakers (Huang et al., 29 Jul 2025)), with balancing by gender, age, and language/variety (Huang et al., 29 Jul 2025, DeRenzi et al., 23 Jul 2025).
• Language Coverage: English, Mandarin Chinese dominate, but up to 46 languages are covered in SpeechFake (Huang et al., 29 Jul 2025) and MD splits (CommonVoice) for cross-lingual studies.
• Generation Method Diversity: Datasets may include up to 40 synthesis algorithms (Huang et al., 29 Jul 2025), spanning text-to-speech (TTS), voice conversion (VC), and neural vocoder (NV) types; tools include GANs, flow-based, diffusion-based, and LLM-centric TTS engines.
• Multimodality: Some datasets are explicitly paired with video (VidTIMIT + TIMIT-TTS (Salvi et al., 2022)) for A/V deepfake research.

Dataset	# Speakers	# Utterances	Hours	Languages	Synthesis Methods
SpeechFake (Huang et al., 29 Jul 2025)	720	>3.3M	>3,000	46	46 TTS/VC/NV tools
SynVox2 (Miao et al., 2023)	5,994	~1.1M	N/A	English	OHNN anonymized, HiFi-GAN
TIMIT-TTS (Salvi et al., 2022)	~40	~80k	~68	English	12 TTS architectures
SynTTS-Commands (Gan et al., 11 Nov 2025)	8,100	384,621	111.3	EN, ZH	CosyVoice 2 (TTS)
DiffSSD (Bhagtani et al., 2024)	75	94,226	~196	EN, ZH	10 diffusion-based synths
Advosynth-500(Deroy, 15 Jan 2026)	5	500	N/A	EN (legal)	Speech Llama Omni

4. Evaluation Protocols and Metrics

Synthetic speech datasets are evaluated by a variety of metrics, depending on research objectives:

• Naturalness and Speaker Similarity: Human Mean Opinion Scores (MOS [1–5], Samsung SOMOS (Maniati et al., 2022), automated UTMOSv2 (Wang et al., 31 Mar 2025)), speaker-embedding cosine similarity (>0.90 for SpkSim (Wang et al., 31 Mar 2025)), and MOSNet/SSL-MOS predictions.
• Intelligibility and Coherence: Word Error Rate (WER), Character Error Rate (CER) for ASR tasks (DeRenzi et al., 23 Jul 2025, Gan et al., 11 Nov 2025, Wang et al., 31 Mar 2025). Content coherence and naturalness now also evaluated through LLM-based scoring (Wang et al., 31 Mar 2025).
• Paralinguistic Fidelity: Pearson correlation of F0 contours, prosody-tag accuracy, paralinguistic MOS (PMOS) (Bai et al., 18 Sep 2025).
• Detection Robustness: Equal Error Rate (EER) for fake/real discrimination and Area Under Curve (AUC) (Huang et al., 29 Jul 2025, Bhagtani et al., 2024, Salvi et al., 2022). For speaker verification tasks, EER quantifies unlinkability and privacy (Miao et al., 2023).
• Fairness and Bias: Fairness Discrepancy Rate (FDR) computed across demographic groups (α-weighted max FAR/FRR deltas) (Miao et al., 2023), gender-disaggregated WER (DeRenzi et al., 23 Jul 2025).
• Task-Specific Metrics: PhonemeF1 (pronunciation-aware F1) for dialogue state tracking (Lee et al., 2023), detection attribution accuracy for identifying synthesis algorithms (Salvi et al., 2022).
• Latency and Resource Metrics: Inference time (ms), energy (μJ), and model size assessed for on-device KWS scenarios (Gan et al., 11 Nov 2025).

5. Privacy, Utility, and Fairness in Synthetic Speech Datasets

A core value proposition of synthetic speech corpora is removal of privacy risk—no biometric or sensitive data from real speakers is present, and speaker identity embeddings can be specifically anonymized (OHNN rotation, pseudo-speaker mapping (Miao et al., 2023)). Privacy is formally captured via unlinkability (cross-EER: ≥30% ideal (Miao et al., 2023)). Utility is weighed by downstream ASV/ASR performance (EER, WER), which can degrade significantly compared to real-speech-only training, but may be ameliorated by utterance-level variability, noise/background mixing, and high-quality TTS models (Miao et al., 2023, DeRenzi et al., 23 Jul 2025).

Fairness receives explicit evaluation via group-disaggregated error rates (FDR), with findings typically reporting small but persistent group-specific error gaps, emphasizing the need for fairness reporting in synthetic dataset releases (Miao et al., 2023, DeRenzi et al., 23 Jul 2025).

6. Downstream Applications and Impact

Synthetic speech datasets are utilized in:

• Speaker Verification and Identification: Both as privacy-preserving alternatives to real datasets and as adversarial benchmarks for robust system design (Miao et al., 2023, Deroy, 15 Jan 2026).
• Speech Deepfake Detection: Major benchmarks for EER benchmarking, open/closed-set generalization, and cross-language/tool robustness (Huang et al., 29 Jul 2025, Bhagtani et al., 2024).
• Dialogue and Conversational Modeling: For audio-based dialogue state tracking, intent recognition, response generation, and cross-modal (A/V) deepfake detection (Lee et al., 2023, Wang et al., 31 Mar 2025, Salvi et al., 2022).
• Command Recognition and TinyML: High-volume multilingual command corpora produced via TTS enable state-of-the-art accuracy in on-device KWS even with micro-joule energy budgets (Gan et al., 11 Nov 2025).
• Low-Resource Language Technology: Rapid synthetic data generation (text+voice) enables ASR and NLP expansion to languages with minimal traditional corpora (DeRenzi et al., 23 Jul 2025).

7. Limitations and Challenges

Synthetic datasets, while scalable and reproducible, exhibit several recurrent challenges:

• Acoustic Diversity Collapse: Speaker and intra-utterance variation is often reduced by anonymization or homogeneous TTS synthesis, depressing downstream model generalization (Miao et al., 2023).
• Partial-Forgery Realism: Segment-level or partially forged speech is less well-modeled; limited datasets exist that explicitly sample such manipulations (Salvi et al., 2022).
• Generalization Across Synthesis Methods: Detectors, even if robust in-domain, may fail catastrophically on new diffusion-based or commercial-quality outputs unless trained with diverse synthetic sources (Huang et al., 29 Jul 2025, Bhagtani et al., 2024).
• Evaluation Set Validity: Real-world error rates are sensitive to test set artifacts, e.g., transcript normalization, accent labels, recording equipment, which may not be faithfully matched in synthetic corpora (DeRenzi et al., 23 Jul 2025).
• Ethical Use and Licensing: While privacy is improved, synthetic datasets must observe the legal constraints of source models and original speech corpora. Open licensing and full pipeline transparency are increasingly required (Huang et al., 29 Jul 2025, Gan et al., 11 Nov 2025, Cuccovillo et al., 2022).

Recent proposals advocate for next-generation datasets offering (1) cross-lingual and demographic diversity, (2) full synthesis method transparency, (3) both studio and in-situ noise/channel conditions, (4) paired real-synthetic splits for each utterance, as well as federated-learning readiness and explainability support (Cuccovillo et al., 2022).

• SynVox2: Towards a privacy-friendly VoxCeleb2 dataset (Miao et al., 2023)
• SpeechDialogueFactory: Generating High-Quality Speech Dialogue Data (Wang et al., 31 Mar 2025)
• SOMOS: The Samsung Open MOS Dataset for the Evaluation of Neural Text-to-Speech Synthesis (Maniati et al., 2022)
• DiffSSD: A Diffusion-Based Dataset For Speech Forensics (Bhagtani et al., 2024)
• SpeechFake: A Large-Scale Multilingual Speech Deepfake Dataset (Huang et al., 29 Jul 2025)
• Open-Source Full-Duplex Conversational Datasets for Natural and Interactive Speech Synthesis (Zhou et al., 4 Sep 2025)
• TIMIT-TTS: a Text-to-Speech Dataset for Multimodal Synthetic Media Detection (Salvi et al., 2022)
• Synthetic Voice Data for Automatic Speech Recognition in African Languages (DeRenzi et al., 23 Jul 2025)
• ADVOSYNTH: A Synthetic Multi-Advocate Dataset for Speaker Identification in Courtroom Scenarios (Deroy, 15 Jan 2026)
• Open Challenges in Synthetic Speech Detection (Cuccovillo et al., 2022)
• SynTTS-Commands: A Public Dataset for On-Device KWS via TTS-Synthesized Multilingual Speech (Gan et al., 11 Nov 2025)
• SynParaSpeech: Automated Synthesis of Paralinguistic Datasets (Bai et al., 18 Sep 2025)
• Exploring the Viability of Synthetic Audio Data for Audio-Based Dialogue State Tracking (Lee et al., 2023)