High-quality speech data for AI systems

High-quality speech data is critical for AI systems that need to sound natural across languages, accents, and contexts. In this case study, Turing delivered studio-grade speech data to support automated dubbing at scale, combining expert human talent with rigorous quality controls to ensure clarity, consistency, and realism. The result: speech data built for real-world production, not demos. Read the full story here https://bit.ly/3MmlFDP

Timestamped Audio Captioning Sonal Kumar et al. Large Audio Language Models struggle to disentangle overlapping events in complex acoustic scenes, yielding temporally inconsistent captions and frequent hallucinations. We introduce Timestamped Audio Captioner (TAC), a model that produces temporally grounded audio descriptions at varying degrees of detail and resolution. TAC is trained with a synthetic data pipeline that constructs challenging and dynamic mixtures from real-world audio sources, enabling robust learning under realistic polyphonic conditions. Across event detection and dense captioning, TAC outperforms all competing methods, with a low hallucination rate and accurate temporal grounding. We also introduce TAC-V, an audio-visual pipeline to generate semantically rich audio-visual descriptions. We then show that TAC and TAC-V serves as a "semantic bridge" for a text-only reasoner: a simple TAC→LLM and TAC-V→LLM cascade achieves state-of-the-art scores on benchmarks for both audio (MMAU-Pro, MMSU, MMAR) and audio-visual (Daily-Omni, VideoHolmes) understanding and reasoning respectively. https://lnkd.in/e3SMEU9X

Voxtral transcribes at the speed of sound. | Mistral AI A product announcement for Mistral’s Voxtral Transcribe 2, featuring state-of-the-art speech-to-text with speaker diarization at $0.003/min and Voxtral Realtime with sub-200ms latency for live transcription. https://lnkd.in/dXMTyKKy

Mimo-audio Enables Few-Shot Learning for Audio Tasks, Scaling to 100 Million Hours Researchers have created a new audio model, MiMo-Audio, that learns to perform a wide range of audio tasks, including realistic speech generation and voice editing, simply by analysing a vast amount of sound data, achieving leading performance among openly available systems and demonstrating an ability to handle tasks it was not specifically trained for. #quantum #quantumcomputing #technology https://lnkd.in/ehFiBr3m

Today we’re making CAMB.AI’s Live Dubbing available to everyone. In this livestream, I’m speaking Italian. The English voice you hear… isn’t mine. And that’s exactly the point. At CAMB.AI, we’ve spent the last two years building live dubbing for high-stakes broadcasts and some of the world’s biggest brands. Now, for the first time, that same product is available to everyone. As a designer in a startup, this was one of those projects where the hardest part wasn’t the technology - it was turning something deeply complex into something that feels simple and natural to use. So yes - I recorded myself in Italian. The English voice is AI. And this is exactly how it works, live. Seeing this go from internal demos and enterprise use cases to a product anyone can try is a really special moment. Proud of the team, proud of what we shipped today - and excited to see how people use it.

I’m genuinely excited about how Speechmatics Speaker Identification is transforming workflows in the broadcasting world. By tagging known voices consistently across recordings from anchors and guests to recurring commentators, we’re not just transcribing audio; we’re unlocking context, searchability, and meaning in media content at scale. Imagine instantly searchable archives where “who said what” isn’t a guess but a given. Or live captioning that accurately tracks speakers in panel discussions and roundtables even in noisy, real-world environments. That’s the kind of capability that can accelerate production, improve accessibility, and elevate audience engagement across newsrooms and studios alike. Check out our documentation here to learn more: https://lnkd.in/eKfPR5hx #SpeechTech #Broadcasting #AI #Speechmatics

 SODA (Scaling Open Discrete Audio) Expanding our R&D beyond Thai language models, this work represents SCB 10X & Typhoon team's latest push into open foundation model development, in close collaboration with Stanford University. We started with a simple question: Can we pre-train a standard transformer — like LLM pre-training — to build a unified backbone where every audio-text task is just next-token prediction? We interleave speech tokens with text tokens at the utterance level — so that audio-text tasks (e.g. speech continuation, ASR, TTS) all become next-token prediction in one model. Along the way, we investigated design choices (data sources, text mixtures, semantic/acoustic token composition) and studied scaling laws for discrete audio models. Two key questions guided the work: (1) how do downstream capabilities change as loss decreases — rapidly, slowly, or plateau? (2) how should we allocate compute between model size and data? We trained 64 IsoFLOP models (3e18–3e20 FLOPs) and found that optimal data grows 1.6× faster than model size. We scaled from 135M to 4B parameters on 500B tokens of fully open data. SODA serves as a unified pre-trained backbone — any audio-language task can be formulated as next-token prediction through prompting and/or fine-tuning. Everything is open: 🏠 Project Page: https://lnkd.in/gGn6qAZH 📄 Paper: https://lnkd.in/g7YCmiyi 🤗 Models & data: https://lnkd.in/gEarJuRY 🎮 Demo: https://lnkd.in/gsbaWtf9 🧪 Experiment log: https://lnkd.in/g_5YSgSq Also, thanks to the Marin project and the Google TPU Research Cloud for supporting this project.

Audio quality can make or break your content. We’ve just released a new version of Voisi, powered by an upgraded Minimax model for voice cloning and text to speech. What’s improved: • More natural, lifelike voice output • Faster generation speed • Clearer, smoother audio delivery For creators, agencies, educators, and businesses producing voice content at scale, this means higher quality output with greater efficiency. If AI voice is part of your workflow, this update is worth your attention. Learn more: https://getvoisi.in/

The Pinch team just submitted a paper to the Interspeech 2026 Audio Encoder Challenge. Carlos Bentes explored whether JEPA-style predictive encoders can serve as a foundation for speech translation systems. Instead of reconstructing audio signals, JEPA models predict representations, which encourages the model to learn conversational structure and timing. The write-up covers the approach, results, and lessons learned along the way. Full blog post: https://lnkd.in/eQ49rjvF

Mistral just released Voxtral Transcribe 2, and the developer response is striking. Real-time transcription with sub-200ms latency. Open weights. CPU-friendly deployment. Support for 13 languages with mid-conversation code-switching. Early developer testing highlights strong real-world performance across complex audio scenarios. But what’s generating the most discussion is deployment flexibility — teams are already experimenting with medical transcription, compliance logging, and meeting intelligence operating entirely locally, without cloud API dependencies. The economics are equally notable. Voxtral is positioned around $0.003/min versus $0.024/min for traditional services — potentially an 8x cost advantage for large-scale speech workloads. For multilingual markets like MENA, where Arabic, English, and French blend naturally in conversation, this could accelerate speech AI moving from feature layer to core enterprise infrastructure. We may be watching speech AI evolve from a service organizations rent to infrastructure they increasingly own and optimize. #SpeechAI #MultilingualAI #AIInfrastructure #EdgeAI #EnterpriseAI #OpenSourceAI