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

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

Kokoro distill 82M -> 7M params

https://huggingface.co/spaces/oddadmix/Kokoro-7M-Distill-Demo

Just another reminder RL is important

https://x.com/kadirnardev/status/2098425551203475665

10000xRT for Zipformer on A100, 18000xRT on H200 with specialized CUDA tricks

https://github.com/SoundsGoodAI/fast-gpu-asr

2.1k parameters VAD

https://github.com/AydinAdnan/PulseVAD

reimplementation of KiloVAD

https://arxiv.org/abs/2607.25870

We see a huge decline of the interest in speech technology in China but a rise in Europe and India. Recently I discussed it with one of my Chinese friends - he confirms that nobody is interested anymore in plain speech. It has to be multimodal - video, music generation, etc.

"百闻不如一见" — hearing something a hundred times is worse than seeing it once

China is ahead of time here.

Another small 260k params TTS, hard to believe into UTMOS 4.0 based on examples but ok

https://github.com/lab-emi/GrainSpeech

https://lab-emi.github.io/GrainSpeech/

https://arxiv.org/abs/2609.18856

An advanced dictation model from Whispr, an interesting part is GPRO over user corrections

https://wisprflow.ai/canto

Voice agents related benchmarks somehow passed our attention, here are few recent ones:

https://github.com/sierra-research/tau2-bench

https://github.com/ServiceNow/eva

tau-Voice: Benchmarking Full-Duplex Voice Agents on Real-World Domains
https://arxiv.org/abs/2603.13686

EVA-Bench: A New End-to-end Framework for Evaluating Voice Agents
https://arxiv.org/abs/2605.13841

Serious issues with Qwen3-TTS stability, long texts and audio prompts break things

https://arxiv.org/abs/2609.16989

https://x.com/RmdW_W/status/2102032894600401231

Taming Long-form Text-to-Speech
Rongxiang Wang, Berkin Durmus, Aysegul Orhon, Eduardo Pacheco, Atila Orhon

Long-form text-to-speech (TTS) enables multi-turn conversations with consistent prosody and higher quality voice cloning from longer reference audio. Recent open-weights autoregressive TTS models such as Qwen3-TTS and VoxCPM2 attain state-of-the-art word error rate (WER) and speaker similarity (SIM) on short-form prompts but significantly deteriorate when used with long-form prompts. We propose Localized Attention-Constrained Inference (LACI), an inference-only method to detect TTS errors in near real-time, roll back to the error onset and regenerate with temporary guardrails, adding negligible computational overhead. Using LACI, we improve worst-of-N WER across 10 RNG seeds for Qwen3-TTS-0.6B from 35.2% to 3.4% on prompts longer than 1500 words, even surpassing its short-form reliability of 5.4\% on prompts with fewer than 500 words. To demonstrate the efficacy of LACI on voice cloning reliability, we propose a sliding-window version of the SIM metric that we call wSIM. wSIM exposes several novel failure patterns that are not captured by SIM. LACI improves worst-of-N wSIM from 0.01 to 0.47 on 120 seconds of reference audio while reducing the rate of catastrophic generations with WER above 30% from 26% to below 1%

https://github.com/SamsungLabs/samsone

Samsone is a family of open Small Audio Language Models (SALMs) for efficient audio understanding. The release includes Samsone-99M, Samsone-134M, and Samsone-356M checkpoints for both on-device and server-side inference.

https://arxiv.org/abs/2609.21666

Samsone: A Family of Open Small Audio Language Models for On-Device Inference
Piotr Masztalski, Michał K. Grzeszczyk, Olaf Sikorski

The success of Large Audio Language Models has driven the development of massive multimodal networks exceeding billions of parameters. However, the demand for privacy-preserving, low-latency processing has shifted focus toward Small Audio Language Models (SALMs) capable of on-device execution. In this paper, we introduce Samsone, a family of SALMs designed for edge computing. Our core model, Samsone-134M, establishes a new state-of-the-art for its size class across multiple benchmarks. We further explore the scaling laws of SALMs by introducing Samsone-99M and Samsone-356M. Despite their compact footprint, the Samsone family delivers performance competitive with models orders of magnitude larger. To foster open research and reproducibility, we train Samsone on publicly available data. We release the training code, model weights, mobile-optimized checkpoints and provide an open-source Android application to demonstrate real-time on-device inference of Samsone.

Parakeet v3 ternary quantization with good accuracy (178mb) 113xRT on CPU

https://huggingface.co/moondream/parakeet-redux

https://huggingface.co/spaces/Krisp-AI/VoiceIsolation-Benchmark

Benchmark for the speech recognition with background speech

Data. 265 recordings in real offices, four call-center floors and cars. Each work and call-center recording follows a fixed structure: primary alone, both speakers, secondary alone, primary alone. Speakers read scripts to give exact ground truth; environments, devices and background speech are real, with no synthetic mixing. Segments are hand-labeled primary / mix / secondary / noise.

Evaluation. 11 STT configurations across 9 engines, streaming and batch, run on raw audio and after voice isolation. WER is computed with jiwer 4.0.0 at corpus level (errors pooled across files, not averaged). Reference and hypothesis go through the same normalization: regex splitting of alphanumeric tokens, then NVIDIA NeMo WFST text normalization, then lowercasing, contraction expansion, punctuation and filler removal. Perceptual quality is scored with DNSMOS-C on primary-speaker segments only.

Results.
Corpus WER: 23.29% → 6.26%
All 11 configurations improve
Engine spread narrows from 17–37% to 4–8%
Clean phone audio regresses slightly: 3.48% → 3.91%

Limitation. On clean narrowband audio there's little to remove, and isolation removes some of the primary signal. We report it rather than filtering it out. Segment-level labels also let you check for deletions specifically: a model that correctly outputs silence and one that drops primary-speaker words can post similar corpus WER.

Dataset: https://huggingface.co/datasets/Krisp-AI/VoiceIsolation-Benchmark-Dataset
Model outputs: https://huggingface.co/spaces/Krisp-AI/VoiceIsolation-Benchmark

Facebook's Muse Transcribe is 1st on AA leaderboard and 25th on Huggingface leaderboard

https://x.com/EricBezzam/status/2102368894929514711

Some recent advanced TTS evaluation

Blog by @altsoph from Inworld
https://altsoph.substack.com/p/wtf-is-voice-steering

J-HARD-TTS-Eval, a benchmark designed to evaluate the robustness of autoregressive Japanese Text-To-Speech (TTS) models (V2 is coming)
https://github.com/Parakeet-Inc/J-HARD-TTS-Eval

EmergentTTS-Eval: Evaluating TTS Models on Complex Prosodic, Expressiveness, and Linguistic Challenges Using Model-as-a-Judge
https://arxiv.org/abs/2505.23009

Yodas3 was shared yesterday. 55TB, 1.1M hours of speech data

https://huggingface.co/datasets/espnet/yodas3

from previous experience not very useful to be honest

Highly efficient audio inference engine

https://github.com/pegainfer-project/pega-omni

custom kernel for Mimi codec, etc, 128 Personaplex streams in realtime on GB300, should be like 30 streams on 4090

Interspeech 2026 starts today

https://www.isca-archive.org/interspeech_2026/

Let us be brave to read all this. Surprisingly, very few agentic papers.