Some modern tech for speech processing on earbuds, interesting talk overall

https://www.youtube.com/watch?v=zeDkT8EuKao

Audio in the wild is still very complex

https://aslp-lab.github.io/SmartGlasses/

https://x.com/MosiAI_Official/status/2090775763666812997

MOSS takes 1st place across all four tasks in the IEEE SLT 2026 SmartGlasses Challenge. The challenge tests whether AI can truly hear and understand real conversations through smart glasses — handling noise, overlapping speech, multiple speakers, and long-context interactions. Among 77 participating teams, MOSS achieved 1st place across both tracks and all four tasks, covering speech recognition and spoken language understanding in real-world smart glasses scenarios.

Results: Two-person TSA-ASR — 5.23% tcpCER

Two-person SLU — 88.8% Accuracy

Multi-party TSA-ASR — 27.95% tcpCER

Multi-party SLU — 93.0% Accuracy

The models behind these results: • MOSS-Transcribe-Diarize for TSA-ASR • MOSS-Audio for SLU

Somehow they forgot about leaderboard

Kytai released pockettts training code

https://github.com/kyutai-labs/pocket-tts/tree/main/training

https://x.com/kyutai_labs/status/2092254286772080768

https://huggingface.co/BreezeBlue/Breeze-TTS-2

https://breezeblue.ai/breeze-tts-2

English/Chinese only but really good quality. 1st place on ArtificalAnalysis leaderboard

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://turnbench.sesame.com/

A multi-domain benchmark for evaluating conversational turn-taking. We hand-annotate end-of-turn and interruption events in dual-channel human conversations, and measure how accurately and how quickly models detect them.

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:

https://www.nature.com/articles/srep12881

Human starts to plan answer 2 seconds before question end

Quite an obvious but so ignored by industry before. There is certainly no need to clone from 3 seconds

You can now create an instant voice clone from up to 2 minutes of reference audio, compared with 20 seconds before.

The longer sample gives the model much more information about the voice, including phonemes, intonation, pauses, rhythm, pacing, emphasis, emotion, and speaking style.

The result:
• Higher voice similarity
• Greater consistency, especially across longer sentences
• Better pronunciation coverage
• More natural prosody
• Better preservation of speaking style

https://www.linkedin.com/posts/soniox_soniox-texttospeech-voiceai-activity-7501594413660401664-p9Cq

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.

Scicom from Malaysia tries Ascend 910B3

https://github.com/Scicom-AI-Enterprise-Organization/TTS-API-Neucodec/blob/main/ASCEND_910B3_PRECISION_REPORT.md

Bodhan AI together with AI4Bharat recently released a great update on Indic ASR

https://bodhan.ai/research/blogs/indic-transcribe

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

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.

https://arxiv.org/abs/2609.08936

Pyannote improved

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

Suplime results on Russian telephony data, good results, first place, large model is better than Diarizen

A bit slow though

Interesting TTS, claims 14ms TTFA

https://github.com/X-Square-Robot/X2Streaming-TTS

https://x.com/XSquareRobot/status/2098015796052242601

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