Speech Technology

he Beyond Transcription Challenge, an IEEE SLT 2026 shared task tackling a foundational question in audio AI: can a model reason over speech without first converting it to text? The research question: Current speech models still struggle to extract meaning directly from audio, especially when the signal includes overlapping speakers, ambient sounds, and room acoustics. Clinical note generation from doctor-patient conversations is an ideal stress test for this: it demands that a model attend to who said what, filter environmental noise, and produce faithful structured output. Yet on the Synth-DoPaCo dataset, end-to-end models hallucinate at alarming rates, with 99–100% of clinical claims unsupported by the source audio, compared to just 21–23% for traditional transcribe-then-summarize pipelines. BeTraC is a shared evaluation challenge aimed at closing this gap by advancing the technology. Two competition tracks: - Lightweight (≤ 6B params): Single end-to-end model, one invocation. Audio in, SOAP note out. - Heavyweight (≤ 36B params): Tools and agents allowed. Only the final model generates text from audio. The Synth-DoPaCo dataset: 8,800 synthetic doctor-patient conversations (~1,329 hrs), 66 ambient sound classes, room reverberation, Opus compression. Available now on Hugging Face. Key dates: - May 4, 2026: Open-Source Inclusion Proposals Deadline - June 24, 2026: System submission deadline - July 8, 2026: Challenge paper due Data is live. Baselines are posted. Team registration is open. If you work on speech, audio understanding, or multimodal AI, we'd love to have you compete. 👇 https://betrac.github.io group:betrac@googlegroups.com

https://github.com/OpenMOSS/MOSS-TTS-Nano 0.1B params, still many languages

The question is when we will see 32B speech model https://huggingface.co/KRAFTON/Raon-Speech-9B

Rissa Cao, FishAudio CEO, a bit of marketing but very valid point on data importance and lack of real high quality data for speech systems https://www.linkedin.com/feed/update/urn:li:activity:7448399470251356160/ Early on, we made a mistake. We trained our TTS model on whatever voice data we could find online. It sounded great on podcasts. But terrible for creation, companionship, anime dubbing. Everything fell apart. The data distribution was wrong.

Recently I was fighting with OOM in ASR service, this was was just on time, very relevant Omni Model Inference: How We Move Tensors Between Stages https://x.com/GenAI_is_real/status/2041723531357196471

Interesting 1.6M TTS engine, based on StyleTTS https://github.com/tronghieuit/tiny-tts

Useful thing as trained on rare private SFX data https://github.com/SonyResearch/Woosh

VoxCPM2 is the latest major release — a 2B parameter model trained on over 2 million hours of multilingual speech data, now supporting 30 languages, Voice Design, Controllable Voice Cloning, and 48kHz studio-quality audio output. Built on a MiniCPM-4 backbone. https://github.com/OpenBMB/VoxCPM

Good talk on SpeechLMs https://www.youtube.com/watch?v=m65SiSnsZ3g Explained the paper below. Basically at different point of time one has to pick different layers from text LM for adapters. Word boundaries require more linguistic knowledge, middle words more acoustic knowledge. Big improvements with adjusted adapters as a result. https://arxiv.org/abs/2503.06211 Late Fusion and Multi-Level Fission Amplify Cross-Modal Transfer in Text-Speech LMs Santiago Cuervo, Adel Moumen, Yanis Labrak, Sameer Khurana, Antoine Laurent, Mickael Rouvier, Phil Woodland, Ricard Marxer Text-Speech Language Models (TSLMs) -- language models trained to jointly process and generate text and speech -- are commonly trained through an early modality fusion/fission approach, in which both modalities are fed and predicted from a shared backbone via linear layers. We hypothesize that this approach limits cross-modal transfer by neglecting feature compositionality -- specifically, the finer-grained nature of speech representations compared to text -- preventing the emergence of a shared feature hierarchy within model layers. In this paper, we argue that this limitation can be addressed through late fusion and fission, with a fission process that accesses both high- and low-level features for speech generation. Our models implementing these principles, SmolTolk, rival or surpass state-of-the-art TSLMs trained with orders of magnitude more compute, and achieve significantly improved cross-modal performance relative to early fusion/fission baselines. Representation analyses further suggest that our method enhances the model's ability to abstract higher-level, more semantic features from speech, and leads to increasingly shared representation spaces across layers.

Interesting community on Reddit https://www.reddit.com/r/VoiceAutomationAI/ will host AMA session with Tony Robinson, one of the most knowledgeable person I know Upcoming AMA with Dr Tony Robinson (Founder Speechmatics) Excited to announce that Dr Tony Robinson will be joining Unio - The Voice AI Community powered by SLNG for a live AMA with builders & founders. If you’re building voice AI, you already know this: it works in demos… and breaks in production. Dr Tony has spent 36+ years in Voice AI, starting in 1989 at Cambridge where he built one of the earliest neural network based speech recognition systems, long before deep learning became mainstream. Today, Speechmatics powers voice AI across 50+ languages, with customers seeing 9x growth in voice agent adoption in 2025. 📅 Date: 27 March ⏰ Time: 10:30 AM PST / 11:00 PM IST 📍 Location: Reddit (r/VoiceAutomationAI) For the next 24 hours, he’ll be answering questions about: • What actually breaks in production voice AI (and how to fix it) • Accents, noise, latency & real-world edge cases • Designing reliable STT-LLM-TTS pipelines • Lessons from 35+ years building speech systems • Where voice AI is really heading (beyond the hype) • What he’d do differently if starting today If you're building in Voice AI, AI agents, or conversational automation, this is a rare opportunity to learn from someone who has been solving these problems for decades. Join the reddit community to drop questions👇 Link in the first comment.

Just another reminder there is no point in ONNX https://github.com/eschmidbauer/moonshine-c source is pure C 825 lines of code, executable is 40kb. It runs ASR just fine.

DiTs are powering modern TTS systems however one rarely mention their issues. Longer training time, higher data requirements. Convolutions still have sense given the data is locally uniform. A research like this still makes sense for us GPU-poor guys https://arxiv.org/abs/2603.09408v1

Nice upsampler - trained for music, supports upsampling from 8khz (important) https://github.com/woongzip1/UniverSR

https://huggingface.co/datasets/ai-coustics/dawn_chorus_en dawn_chorus_en An open-source evaluation dataset for accurate foreground speaker transcription. The dataset targets mixture conditions where foreground speech remains generally transcribable by speech-to-text systems, while background speech is distinctly perceived as background. It provides around 90 minutes of foreground–background speech mixtures composed of recorded and synthesized foreground speech, along with ground truth foreground speech and corresponding transcripts. Inspired by DAPS, which frames speech enhancement as a direct transformation from real-world device recordings to professionally produced studio speech via aligned input–output pairs, we design this dataset around an equally application-driven mapping: from realistic foreground–background speech mixtures to isolated primary-speaker speech that remains robustly transcribable by downstream STT systems. Like DAPS, our approach emphasizes time-aligned references and real recording / transmission conditions rather than purely synthetic degradations, enabling evaluation of suppression strength versus foreground speech distortion.

Reasoning in audio LLMs is a problem https://github.com/Blinorot/ALARM https://arxiv.org/abs/2603.09556 This is the official implementation of ALARM: Audio–Language Alignment for Reasoning Models, an audio reasoning language model trained in a self-generation setup that achieves state-of-the-art performance on Speech Understanding benchmarks with a 4B backbone. Abstract: Large audio language models (ALMs) extend LLMs with auditory understanding. A common approach freezes the LLM and trains only an adapter on self-generated targets. However, this fails for reasoning LLMs (RLMs) whose built-in chain-of-thought traces expose the textual surrogate input, yielding unnatural responses. We propose self-rephrasing, converting self-generated responses into audio-understanding variants compatible with RLMs while preserving distributional alignment. We further fuse and compress multiple audio encoders for stronger representations. For training, we construct a 6M-instance multi-task corpus (2.5M unique prompts) spanning 19K hours of speech, music, and sound. Our 4B-parameter ALM outperforms similarly sized models and surpasses most larger ALMs on related audio-reasoning benchmarks, while preserving textual capabilities with a low training cost. Notably, we achieve the best open-source result on the MMAU-speech and MMSU benchmarks and rank third among all the models.

Google DeepMind released African ASR/TTS data, somewhat interesting The WAXAL dataset is a large-scale multilingual speech corpus for African languages, introduced in the paper WAXAL: A Large-Scale Multilingual African Language Speech Corpus. https://huggingface.co/datasets/google/WaxalNLP