Text-to-Speech: Advancements in Expressive, Controllable, and Secure Audio Generation

Latest 12 papers on text-to-speech: Jan. 17, 2026

The landscape of Text-to-Speech (TTS) technology is evolving at an unprecedented pace, transforming how we interact with machines and create digital content. Once limited to robotic monotones, recent breakthroughs are enabling remarkably natural, expressive, and controllable speech synthesis. This surge in innovation, driven by advanced AI/ML models, addresses long-standing challenges in fidelity, real-time performance, multilingual support, and even the critical area of deepfake detection and defense.

The Big Idea(s) & Core Innovations

At the heart of these advancements lies the pursuit of highly controllable and realistic audio generation. A standout theme is the disentanglement of speech characteristics, allowing for independent manipulation of style, timbre, and content. The paper, FlexiVoice: Enabling Flexible Style Control in Zero-Shot TTS with Natural Language Instructions, from The Chinese University of Hong Kong and Huawei Technologies Co., Ltd., introduces FlexiVoice, a system that achieves precise style control in zero-shot TTS using natural language instructions. Their Progressive Post-Training (PPT) framework tackles the Style-Timbre-Content conflict, a crucial step toward truly flexible synthesis.

Building on this, ReStyle-TTS: Relative and Continuous Style Control for Zero-Shot Speech Synthesis by researchers from Zhejiang University and Ant Group, among others, proposes ReStyle-TTS, offering continuous and reference-relative style control. This means users can intuitively adjust styles (e.g., make a happy voice sound a bit angrier) without needing perfectly matched reference audio, a significant user experience improvement. Similarly, Segment-Aware Conditioning for Training-Free Intra-Utterance Emotion and Duration Control in Text-to-Speech from the National University of Singapore pushes the boundaries of intra-utterance control, enabling fine-grained emotion and duration shifts within a single spoken sentence without retraining models, a truly groundbreaking “training-free” approach.

The underlying technology powering much of this realism comes from score-based generative models, as highlighted in Audio Generation Through Score-Based Generative Modeling: Design Principles and Implementation by Ge Zhu, Yutong Wen, and Zhiyao Duan from the University of Rochester. They provide a unifying framework, demonstrating that principled training and sampling practices from image diffusion models can be effectively transferred to audio, enhancing generation quality and conditioning flexibility. This foundational work underpins many high-fidelity audio applications.

Beyond generation, the equally critical area of deepfake detection and robust security is seeing rapid development. The ESDD2: Environment-Aware Speech and Sound Deepfake Detection Challenge Evaluation Plan by Xueping Zhang (University of Science and Technology, China) emphasizes the necessity of leveraging environmental cues to detect increasingly realistic deepfakes. This is crucial as models like VocalBridge, presented in VocalBridge: Latent Diffusion-Bridge Purification for Defeating Perturbation-Based Voiceprint Defenses, are designed to bypass voiceprint defenses using advanced latent diffusion models, underscoring the ongoing arms race between generative AI and security measures.

Addressing challenges in specific domains, Stuttering-Aware Automatic Speech Recognition for Indonesian Language by authors from Universitas Indonesia leverages synthetic data augmentation to improve ASR performance for stuttered speech in low-resource languages. Their finding that fine-tuning on synthetic stuttered data alone outperforms mixed training is a powerful insight for inclusive AI. In a similar vein, Domain Adaptation of the Pyannote Diarization Pipeline for Conversational Indonesian Audio, also from Universitas Indonesia, showcases how synthetic data can significantly boost speaker diarization performance for low-resource conversational audio, effectively bridging the gap between English-centric models and other languages.

Under the Hood: Models, Datasets, & Benchmarks

These innovations rely on powerful models, meticulously crafted datasets, and rigorous benchmarks:

• FlexiVoice-Instruct Dataset: Introduced by FlexiVoice, this large-scale, diverse speech dataset is annotated using LLMs to support multi-modality instruction-based TTS, crucial for flexible style control.
• AudioDiffuser: The open-source codebase from the “Audio Generation Through Score-Based Generative Modeling” paper, available at https://github.com/gzhu06/AudioDiffuser, provides key components for implementing score-based audio generation frameworks, fostering reproducible research.
• SPAM (Style Prompt Adherence Metric): Proposed in SPAM: Style Prompt Adherence Metric for Prompt-based TTS by Chung-Ang University researchers, SPAM is an automatic metric using a CLAP-inspired approach and supervised contrastive loss. It ensures both plausibility and faithfulness in evaluating how well synthesized speech adheres to style prompts, aiming to replace human Mean Opinion Score (MOS) evaluations.
• SPEECHMENTALMANIP: From Columbia University and Red Hat, in Detecting Mental Manipulation in Speech via Synthetic Multi-Speaker Dialogue, this synthetic multi-speaker speech benchmark helps detect mental manipulation in spoken dialogues, isolating modality effects. The associated code is at https://github.com/runjchen/speech_mentalmanip.
• CompSpoofV2 Dataset & ESDD2 Challenge: The ESDD2 challenge introduces CompSpoofV2, an extensive benchmark dataset with over 250,000 audio clips (283 hours) for environment-aware deepfake detection. Baseline models and evaluation metrics are available at https://github.com/XuepingZhang/ESDD2-Baseline.
• IndexTTS 2.5: Bilibili Inc.’s IndexTTS 2.5 Technical Report details an enhanced multilingual zero-shot TTS model, leveraging semantic codec compression and Zipformer architecture for faster, higher-quality, and multi-language emotional synthesis.
• Synthetic Stuttering Data for Indonesian: The “Stuttering-Aware ASR” paper utilized a synthetic data augmentation framework, with code at https://github.com/fadhilmuhammad23/Stuttering-Aware-ASR, to generate stuttered Indonesian speech, showcasing the power of synthetic data in low-resource settings.

Impact & The Road Ahead

These advancements herald a new era for AI-generated audio, promising more natural, user-friendly, and secure interactions. The ability to precisely control emotions, speaking styles, and even specific segments within an utterance opens doors for highly customized virtual assistants, immersive storytelling, and dynamic content creation. Multilingual and low-resource language support means these technologies can serve a global audience more effectively and inclusively.

However, the rise of sophisticated deepfakes, as exemplified by VocalBridge, also underscores the urgent need for robust detection mechanisms and ethical deployment guidelines. The focus on environment-aware detection (ESDD2) and the continuous development of evaluation metrics like SPAM are critical steps in this arms race. The integration of LLMs in areas like hate speech recognition (LLMs-Integrated Automatic Hate Speech Recognition Using Controllable Text Generation Models) also highlights how interdisciplinary approaches are crucial for tackling complex societal challenges.

The road ahead will undoubtedly involve further refinement of control mechanisms, improved real-time performance, and a stronger emphasis on ethical AI and security. As these papers collectively demonstrate, the synergy between generative models, sophisticated evaluation, and targeted domain adaptation is paving the way for a future where synthesized speech is virtually indistinguishable from human speech, empowering creators and communicators while demanding vigilance in its application.