A Closer Look at Phonon TTS. Launching High-Quality On-Device Speech That Tops the Benchmarks.

TTS Arena: Benchmarking Text-to-Speech Models in the Wild

Introducing the Realtime API

Developers can now build fast speech-to-speech experiences into their applications

TTS-Q: Speaker-Aware Post-Training Quantization of Text-to-Speech Diffusion Transformers

The paper presents TTS‑Q, a post‑training quantization method that reduces the size of large diffusion transformer TTS models to 4‑bit precision while preserving speaker timbre and intelligibility. By identifying hard‑to‑quantize weights and activations and applying a mixed‑precision schedule that gives higher precision to late‑step feed‑forward activations, TTS‑Q restores timbre loss observed in standard 4‑bit PTQ. The approach also offers a calibrated‑W4A4 reference that lowers GPU memory usage, making TTS diffusion transformers more viable for on‑device, assistive, and real‑time applications.

TTS-Q: Perception-Guided Post-Training Quantization of Text-to-Speech Diffusion Transformers

The paper introduces TTS‑Q, a perception‑guided post‑training quantization method for zero‑shot text‑to‑speech diffusion transformers. It identifies that 4‑bit weight‑and‑activation quantization erodes speaker timbre, and addresses this by selecting hard‑to‑quantize items and applying a mixed‑precision schedule that gives higher precision to late‑step feed‑forward activations. The resulting approach restores timbre while preserving intelligibility and reduces GPU memory usage for resource‑constrained deployment.

ZONOS2 Technical Report

We present ZONOS2 8B, our latest TTS model, which achieves state-of-the-art naturalness, prosody, and voice cloning fidelity. We improve upon Zonos-v0.

CN-NewsTTS Bench: a target-level automatic benchmark for raw-input Chinese news TTS pronunciation

Chinese news text contains dense written forms such as scores, hyphenated model names, ranges, unit symbols, percentages, English abbreviations, and mixed Chinese-Latin-digit names. These forms are frequent in real listening workflows, and a text-to-speech (TTS) system can preserve the written string while changing the spoken meaning.

P2Flow: Phoneme-aware Progressive Flow Matching for Extreme Speech Super-Resolution

arXiv:2609.24138v1 Announce Type: cross Abstract: Generative models have recently demonstrated considerable promise in speech super-resolution (SSR). Nevertheless, the majority of existing work has c...

Measuring benchmark optimization in speech recognition

dots.tts Technical Report

arXiv:2606. 07080v1 Announce Type: cross Abstract: We present dots.

SoniSpeech: A Large-Scale Open-Vocabulary Tri-Modal Dataset for Wearable Silent Speech Interfaces

arXiv:2608. 00803v1 Announce Type: cross Abstract: Wearable silent speech interfaces (SSIs) are limited to small, closed vocabularies.

Scaling Audio Models Efficiently: Joint Optimization of Scale, Resolution, Adaptation, Precision, and Sparsity

The paper introduces a compression framework for the Whisper automatic speech recognition model that jointly optimizes six deployment dimensions—model size, temporal resolution, encoder token stride, low‑rank adaptation capacity, weight precision, and sparsity pattern—using NSGA‑III. The optimization targets three objectives: word error rate, inference FLOPs, and memory footprint. Evaluating 1,680 configurations, the study identifies compression combinations that outperform single‑axis scaling and notes that 1:4 structured sparsity cannot maintain acceptable accuracy within the tested budgets.

ZONOS2 Technical Report

arXiv:2606. 24320v1 Announce Type: cross Abstract: We present ZONOS2 8B, our latest TTS model, which achieves state-of-the-art naturalness, prosody, and voice cloning fidelity.