Speech Technology

2.1k parameters VAD https://github.com/AydinAdnan/PulseVAD reimplementation of KiloVAD https://arxiv.org/abs/2607.25870

10000xRT for Zipformer on A100, 18000xRT on H200 with specialized CUDA tricks https://github.com/SoundsGoodAI/fast-gpu-asr

Just another reminder RL is important https://x.com/kadirnardev/status/2098425551203475665

Kokoro distill 82M -> 7M params https://huggingface.co/spaces/oddadmix/Kokoro-7M-Distill-Demo

https://x.com/unilightwf/status/2098261200480174123 https://arxiv.org/abs/2603.14328 Cross-lingual cloning TTS is still a big problem, some accent metrics demo interesting results Also worth checking https://iwslt.org/2026/voice-cloning with some useful data https://huggingface.co/datasets/ymoslem/acl-6060

My friend Miro recommended me Orukeet model https://github.com/Oruk-AI/orukeet https://arxiv.org/abs/2609.10054 it is indeed a good parakeet improvement, about 10% better. Interesting that people left scaling and return back to in-depth architecture analysis. Oruk.AI does some other nice things, for example a visualization of emotion representation in different layers of speech models https://x.com/OrukLabs/status/2073457781018087473 https://oruk.ai/research/how-models-represent-speech

Interesting TTS, claims 14ms TTFA https://github.com/X-Square-Robot/X2Streaming-TTS

Suplime results on Russian telephony data, good results actually second after Diarizen Large A bit slow though

Pyannote improved https://github.com/rewayai/suplime/

https://huggingface.co/tencent/AuK AuK is a 1.5B foundation model for speech generation and editing. Trained on millions of hours of diverse audio data, AuK supports zero-shot and instruction-based TTS, content and acoustic editing, paralinguistic editing, speech enhancement, and source separation through a unified natural-language instruction interface.

ParsVoice, the largest open-source Persian speech dataset, along with a TTS model and an open-source processing pipeline is released. The paper has also been accepted as a main conference paper at EMNLP 2026. Paper: https://arxiv.org/abs/2510.10774 Dataset: https://huggingface.co/datasets/MohammadJRanjbar/ParsVoice TTS model: https://huggingface.co/MohammadJRanjbar/ParsVoice-XTTS Code & pipeline: https://github.com/MohammadJRanjbar/ParsVoice

Bodhan AI together with AI4Bharat recently released a great update on Indic ASR https://bodhan.ai/research/blogs/indic-transcribe

Scicom from Malaysia tries Ascend 910B3 https://github.com/Scicom-AI-Enterprise-Organization/TTS-API-Neucodec/blob/main/ASCEND_910B3_PRECISION_REPORT.md

https://arxiv.org/abs/2609.01246v1 Ready to Speak: Aligning LLMs for TTS-Friendly Text Generation Thibaut Thonet, Jos Rozen, Laurent Besacier Current Large Language Models (LLMs) are primarily optimized for written text, often producing outputs that are grammatically correct and helpful yet poorly suited for spoken delivery via Text-to-Speech (TTS). In this work, we study how to make LLMs natively generate TTS-friendly text, which we frame as a preference alignment problem: instead of relying on downstream rewriting modules, we directly align LLMs to generate text optimized for spoken delivery. We introduce two preference datasets spanning different target domains, CORA and Recipe, which contain paired TTS-friendly and TTS-unfriendly responses. We further propose an evaluation suite combining a pattern-based heuristic metric, a TTS→ASR evaluation pipeline, and a MUSHRA listening study with human judges. Our experiments compare the recently proposed Feature-aware Sampling and Tuning (FaST) framework -- leveraging interpretable features instead of a black-box reward model -- against an array of alignment baselines on the TTS-friendly generation task. Notably, we found that FaST achieves the best overall tradeoff between TTS-friendliness and helpfulness across various settings. We also identified a strong correlation between our different metrics, highlighting the ability to reliably assess TTS-friendliness via an efficient heuristic.

Quite an obvious but so ignored by industry before. There is certainly no need to clone from 3 seconds https://www.linkedin.com/posts/soniox_soniox-texttospeech-voiceai-activity-7501594413660401664-p9Cq

https://www.nature.com/articles/srep12881 Human starts to plan answer 2 seconds before question end

We know human scores are useless but anyway https://x.com/datapointai/status/2094829412625654141 today, we're releasing the largest open-source human audio preferences dataset, focused on the customer support use-case - 300K+ annotations by real people - 15 SOTA TTS models ranked (Sonic 3.6, Grok TTS, Simba 3.2, Eleven Labs v3) - 8 categories (IVR menus, empathy, escalations, refunds etc) dataset + benchmark + frontier plot below:

You can download really huge datasets these days https://x.com/ahochlehnert/status/2092648676829413778 LAION-BVD: a 10-million-hour open video dataset for multimodal pre-training. - 1.3B video URLs from CommonCrawl - 80M downloaded videos - 10M video hours - 55M captioned clips - 300M frame-caption pairs This repository contains 1.7 million audio clips taken from BVD-V-55M and sampled for uniqueness of the source video, so that the subset maximises source diversity rather than clip count. Each clip comes with a caption, its language, and the timestamps locating it in the source video. https://huggingface.co/datasets/laion/BVD-A-1.7M

https://huggingface.co/BreezeBlue/Breeze-TTS-2 https://breezeblue.ai/breeze-tts-2 English/Chinese only but really good quality