Good Paper from Interspeech and dataset for game developers

https://huggingface.co/datasets/NCSOFT/Designed-Vocalizations-Dataset

https://ncai-official.github.io/speech/publications/designed-vocalizations-dataset/index.html

Advances in AI-based voice conversion have enabled a wide range of media applications, including films, audiobooks, and games. However, most research and public benchmarks still focus on natural human speech, leaving designed vocalizations such as monster growls and robotic voices underexplored, partly due to the lack of publicly available resources. To address this gap, we introduce the Designed Vocalizations Dataset, created by applying professional vocal effects processing to diverse vocal sources to produce paired original and effect-modified audio. We further provide a standardized test set with explicit seen/unseen splits over source types and preset styles to assess generalization under controlled conditions, together with baseline benchmark results for reproducible evaluation.

New update from PocketTTS, different objective improves things

https://kyutai.org/blog/2026-09-28-pocket-tts-drifting/

You can inject directly into KV cache ;)

Symbiotic Architecture for Post-Hoc Audio Extension of Frozen Language Models
https://arxiv.org/abs/2609.30784

Popular thing in modern LLMs, Mostik is doing similar research, also

Cache-to-Cache: Direct Semantic Communication Between Large Language Models
https://arxiv.org/abs/2510.03215

The Conversational AI Reading Group is back for Fall 2026!

Over the past two years, we've hosted more than 50 talks from researchers in academia and industry working on speech, audio, and conversational AI, and we're excited to continue this season.

We're kicking off this week with Sreyan Ghosh who will talk about building open audio-language models:

Towards Fully Open General Audio Intelligence
Thursday, October 8 | 11:00 AM – 12:00 PM ET
Speaker: Sreyan Ghosh - Google DeepMind
Details, Zoom link, and the full schedule of upcoming talks: https://poonehmousavi.github.io/rg.html
Youtube: https://www.youtube.com/@CONVAI_RG

Our friend @RND_RandoM recently released a cool full-duplex voice agent setup which that rivals GPT-Live

https://github.com/speakrail/speakrail

The pipeline consists of:

STT: Voxtral Realtime with an attached turn head (HF), running on our audio.cpp fork.
LLM: Gemma 4 12B QAT with microturn finetuning (HF). It is chosen because it fits the GPU quite well, has vision support (I want to test it soon), and in general, the Gemma models perform well in real-life tasks, general chatting, etc.
TTS: Breeze TTS 2, patched to run at int8 (GitHub fork), although it can be replaced by any streaming TTS.
The harness itself: it is the glue between all the components, and has many latency-saving measures, like speculative LLM+TTS firing (inspired by HF speech-to-speech).

The cool thing is that Danil took inspiration from several "think while talking" papers (e.g. SHANKS): while you are talking, a base Gemma 4 12B int4 writes thinking notes, which are then passed to the talker. It helps with harder tasks that require more reasoning.

Please try it out

I have a thought why @JiliJeanlouis got back to us and here is the answer

https://www.linkedin.com/posts/big-news-gladia-has-been-acquired-by-ovhcloud-ugcPost-7507771273087905792-64cN/

Big news: Gladia has been acquired by OVHcloud. 🚀

Congratulations! Hope he will have more time for us now!

Nobody yet released SpeechJev

https://x.com/Tu7uruu/status/2108164283477471309

Neuphonic just released NeuDecide, an audio-first tiny decision model at only 43 MB, running on a single CPU thread!!

> Speech directly to tool calls (no transcription needed)
> Just 46 ms inference on an M3 MacBook
> Runs fully offline, even on mobile & Raspberry Pi
> 75.5% tool accuracy on SLURP

Towards Fully Open General Audio Intelligence
Speaker: Sreyan Ghosh - Google DeepMind

https://www.youtube.com/watch?v=n1QzhX_u-o0

watched this talk quickly, just couple points

1. Audio Flamingo injects audio features with cross-attention, not as tokens (not like speech LLMs - qwen, granite)
2. End to end was not successful (as usual, starts 48:10)

Tatiana left Apple and now works at Antropic

https://www.linkedin.com/in/tatiana-likhomanenko-36905925a

there are rumors about Antropic video/audio models too, hopefully we will see them soon.

https://huggingface.co/fishaudio/drama-3

35B params 3B active

Poor me reading all the Claude outputs literally everywhere. I hate it already. I barely understand the meaning. Guys, please do something about it. Help!

Few examples:

...The ElevenLabs rows are a control, not a complaint

...All 2,600 outputs remain in the evaluation. Twelve outputs reached the 512-token cap: 5 Kyrgyz, 3 Kazakh and 4 Russian (0.46% overall)

...so the choice of reward model is itself a robustness decision.

https://x.com/_josh_meyer_/status/2110043334182043658

How to kill a voice agent? Just turn on the TV

We benchmarked 14 voice agents with a TV playing in the background.

Task completion fell from 38.7% to 8.6%

Irodori is Japanese only but overall pretty advanced TTS. Here T5Gemma text encoder is particularly interesting

https://huggingface.co/Aratako/Irodori-TTS-v4-Large

Drifting is not a keyword anymore, we look for priming

https://x.com/Jaeyeon_Kim_0/status/2110744814274674851

https://github.com/JaeyeonKim01/priming

Dear colleagues,

We are happy to announce that The Sixth Workshop on NLP Applications to Field Linguistics (Field Matters 2027) will take place at EACL 2027 on March 9-14 in Athens, Greece (online participants are also welcome).

We accept papers on the following topics:
·         Application of NLP to field linguistics workflow;
·         The impact, benefits, and harms of NLP-assisted fieldwork;
·         Transfer learning for under-resourced language processing;
·         The use of fieldwork data to build NLP systems;
·         Modeling morphology and syntax of typologically diverse languages in the low-resource setting;
·         Speech processing for under-resourced languages;
·         Machine-readable field linguistic datasets and computational analysis of field linguistics datasets;
·         Using technology to preserve culture via language;
·         Improving ways of interaction with Indigenous communities.

Important dates
Direct Submission deadline: December 15, 2026
Pre-reviewed (ARR) submission deadline: December 22, 2026
Notification of acceptance: January 11, 2027
Camera-ready deadline: January 22, 2027
Workshop dates: March 9-14, 2027
You can find more information on the submission process and format requirements on our website.

For our 2027 edition, Field Matters will organise a Shared Task on Automatic Interlinear Glossing. More details can be found on the dedicated page.

If you have any questions, feel free to ask them!

COT-TTS: Audio Context-Aware Text-to-Speech with Chain-of-Thought Reasoning

https://luckybian.github.io/COT-TTS/

Similar ideas, TTS properties could be more predictable and explainable

https://huggingface.co/WhissleAI/steer-tts

Recently a good Arabic ASR was released:

https://huggingface.co/IFM/Jais-ASR

Good results on telephony, just a little behind the public leader (Openai GPT Audio 1.5) but still significantly behind custom finetuned models. For huge volume and smaller domain a simple conformer/zipformer can do much better than any advanced model but might fail on proper names and complex vocabulary.

Some other leaderboard results (not complete) here:

https://huggingface.co/spaces/elmresearchcenter/open_universal_arabic_asr_leaderboard

https://lnkd.in/p/eh-i56jw

https://opensvbench.xyz/

https://arxiv.org/abs/2610.13760

OpenSVBench: Diagnosing Speaker Verification Generalization Across Deployment Scenarios
Qituan Shangguan, Bosen Xu, Shuai Wang

Speaker verification (SV) systems now achieve very low error rates on standard public protocols, but those results do not fully describe deployment generalization. VoxCeleb-style evaluations remain useful anchors for English in-the-wild media speech. Scenario-specific evaluations and challenges are also valuable. However, neither setting directly shows whether the same system remains reliable across diverse deployment scenarios. We introduce OpenSVBench, a public benchmark for cross-scenario SV evaluation. Its fixed public core contains 26 trial sets organized into 11 scenario groups, with 9.15M verification trials, frozen target/non-target keys, and metadata for scenario-wise reporting. Around this core, the online leaderboard acts as a diagnostic evaluation interface rather than only a ranked list. It accepts score packages from new systems, evaluates them with frozen keys and common scoring, and reports trial-set breakdowns, scenario-group metrics, macro EER/minDCF, model-wise profiles, and scenario-wise comparisons. We evaluate 18 public checkpoints spanning x-vector, ECAPA-TDNN, CAMPPlus, ResNet and ERes variants, ReDimNet2, SAM-ResNet, and pretrained Transformer speaker recipes. The results show clear cross-scenario gaps: systems with similar macro EER can have different scenario profiles, strong performance in one scenario does not guarantee strong performance in another, and same-scenario trial sets can still expose source and protocol gaps.

Recently released MingTTS supposed to be good and just 0.5B params. And some other models too:

https://x.com/AntLingAGI/status/2112210811997540475

Today, we're open-sourcing 5 Ming-3.0 models across 3 speech families.

- Ming-3.0-tts: 0.5B voice design & paralinguistic cues; Tiny 7.9B A1.3B long-form speech.
- Ming-3.0-audio: Flash 126B A6.5B understanding & tools; Tiny 9.3B A2.7B efficient dialogue.
- Ming-3.0-asr: Tiny 8.6B A2B dialects & context.