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A Survey on Neural Speech Synthesis
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Text to speech (TTS), or speech synthesis, which aims to synthesize intelligible and natural speech given text, is a hot research topic in speech, language, and machine learning communities and has broad applications in the industry. As the development of deep learning and artificial intelligence, neural network-based TTS has significantly improved the quality of synthesized speech in recent years. In this paper, we conduct a comprehensive survey on neural TTS, aiming to provide a good understanding of current research and future trends. We focus on the key components in neural TTS, including text analysis, acoustic models and vocoders, and several advanced topics, including fast TTS, low-resource TTS, robust TTS, expressive TTS, and adaptive TTS, etc. We further summarize resources related to TTS (e.g., datasets, opensource implementations) and discuss future research directions. This survey can serve both academic researchers and industry practitioners working on TTS.
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Cited by 19 Pith papers
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Bagpiper-TTS: Natural Language Guided Universal Speech Synthesis
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Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers
VALL-E is a neural codec language model trained on 60K hours of speech that performs zero-shot TTS, synthesizing natural speech that matches an unseen speaker's voice, emotion, and environment from a 3-second prompt.
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Probing Speaker Identity Sensitivity in Audio Deepfake Detectors
A new Identity Sensitivity Score flags misclassified audio deepfake detections with AUC up to 0.954, but its claim to isolate speaker-identity behavior from plain confidence is not yet controlled.
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DisSpeech: Low-Resource Controllable Mandarin Stuttered Speech Synthesis for ASR Augmentation
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Evaluating Generalization and Robustness in Russian Anti-Spoofing: The RuASD Initiative
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NVSpeech: An Integrated and Scalable Pipeline for Human-Like Speech Modeling with Paralinguistic Vocalizations
A Mandarin speech pipeline that tags non-word vocalizations (laughter, breath, filled pauses) for ASR and controls their generation in TTS, backed by a claimed 573-hour, 174,179-utterance word-level annotated corpus.
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SpeechFake: A Large-Scale Multilingual Speech Deepfake Dataset Incorporating Cutting-Edge Generation Methods
SpeechFake is a large-scale multilingual deepfake speech dataset with baseline experiments showing improved generalization to unseen generation methods.
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Domain-Specific Evaluation of Text-to-Speech Systems: A Multi-Metric Benchmarking Study
No single Urdu TTS system wins across all metrics, and the system listeners liked most, Google Gemini, was the furthest from reference audio on objective acoustic scores.
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Zero-Shot Face-to-Speech Synthesis via Latent Space Adaptation of a Style-Diffusion TTS Model
A lightweight face adapter plus soft-tuning aligns face embeddings to a frozen StyleTTS 2 style space, yielding natural zero-shot face-to-speech and language-agnostic transfer to Spanish.
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Position: Towards Responsible Evaluation for Text-to-Speech
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Speaker-Conditioned Phrase Break Prediction for Text-to-Speech with Phoneme-Level Pre-trained Language Model
Speaker-conditioned phrasing with phoneme-level PLMs (MP BERT) improves pause prediction from F0.5 0.3719 to 0.4991, and a few-shot adapter generalizes to unseen speakers.
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One Voice, Many Tongues: Cross-Lingual Voice Cloning for Scientific Speech
A system based on OmniVoice with multi-model ensemble distillation for fine-tuning shows consistent gains in intelligibility metrics while keeping speaker similarity for cross-lingual scientific speech.
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XR-CareerAssist: An Immersive Platform for Personalised Career Guidance Leveraging Extended Reality and Multimodal AI
XR-CareerAssist fuses XR and five AI modules into a Unity-based immersive platform for multilingual, personalized career guidance via 3D avatars and dynamic Sankey diagrams, reporting 78.3% user satisfaction in a 23-p...
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Marco-Voice Technical Report
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One Voice, Many Tongues: Cross-Lingual Voice Cloning for Scientific Speech
Authors submit a cross-lingual voice cloning system to IWSLT 2026 using OmniVoice fine-tuned on ensemble-distilled synthetic data, reporting gains in WER, CER, and speaker similarity for scientific texts in three languages.
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Voice Mapping of Text-to-Speech Systems: A Metric-Based Approach for Voice Quality Assessment
Voice range indicates TTS model capability with VITS highest, Glow-TTS best at soft phonation, and CPPs of 7-8 dB marking natural quality while values over 10 dB sound robotic.
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