Speech Technology

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

Two talks uploaded, interesting information in both: AudioLLMs (no hope compared to text ones) https://www.youtube.com/watch?v=BJ3L0Kmz7Jw Meeting transcription. LLMs are still bad at diarization, specialized systems (Diarizen + SE-Dicow) are much better https://www.youtube.com/watch?v=2iIXUEnVkAA

IWSLT 2026 has some interesting competitions (like subtitling) with data available for download https://iwslt.org/2026/subtitling Evaluation period starts April 1st

Fishaudio financials (and mention of S2) https://x.com/rissa_cao/status/2029236698018914456

Or friend @vancheeck recently pushed a new generation of an outstanding speaker identification architecture https://github.com/PalabraAI/redimnet2 It is great this project continues in Palabra https://www.palabra.ai

Interesting job, those are rare nowdays Bland.ai builds AI voice agents that handle real phone calls for some of the largest companies in the world. Our software runs inside critical workflows at companies like Samsara, Gallup, TripAdvisor, Snapchat, Signant Health, Better.com, and others. We have raised $65 million from top Silicon Valley investors including Emergence Capital, Scale Venture Partners, Y Combinator, and the founders of Twilio, Affirm, and ElevenLabs. We are expanding our research team as we train and deploy our own TTS and STT models in production. We are also investing heavily in next generation speech to speech and speech inference systems. We are currently hiring for two roles: Research If you have designed and trained your own models, published papers or in depth technical writing, and are working at the leading edge of audio research, we would love to hear from you: https://jobs.ashbyhq.com/bland/d2e08077-61f0-4810-bc72-3efd7944647b You might be a strong fit if you have experience with: - Large scale TTS, STT, or neural audio codec systems - Self supervised learning, generative modeling, or multimodal modeling - Neural audio codecs, discrete or continuous latent representations, and compression tradeoffs - Running tight ablations and controlled experiments that move ideas from hypothesis to validation quickly - Optimizing inference for real time, low latency production systems Machine Learning Engineer If you are a strong programmer who enjoys building terabyte scale datasets, designing training pipelines, and working on model inference and deployment, while staying closely connected to research, apply here: https://jobs.ashbyhq.com/bland/05906608-0628-412c-8b01-a050d87986c5 If you have any questions please feel free to shoot me a DM!

Good TTS speedups https://github.com/andimarafioti/faster-qwen3-tts

Modern flow matching https://github.com/Aratako/Irodori-TTS rectified flow + dacvae + text encoder with emojis Samples of cloning demo noticable noise btw, seems like DACVAE is not that great.

No model weights, but somewhat interesting ideas. Transfusion: Transfusion (Zhou et al., 2025) was originally proposed in computer vision to develop a model that can jointly perform generation and understanding tasks. https://arxiv.org/abs/2602.17097 AudioChat: Unified Audio Storytelling, Editing, and Understanding with Transfusion Forcing William Chen, Prem Seetharaman, Rithesh Kumar, Oriol Nieto, Shinji Watanabe, Justin Salamon, Zeyu Jin Despite recent breakthroughs, audio foundation models struggle in processing complex multi-source acoustic scenes. We refer to this challenging domain as audio stories, which can have multiple speakers and background/foreground sound effects. Compared to traditional audio processing tasks, audio stories introduce new layers of semantic, temporal, and physical complexity. To address this challenge, we propose AudioChat, a framework for developing audio foundation models that can generate, edit, and understand audio stories. AudioChat introduces a new paradigm in which LLM-based toolcalling agents simulate interactions between users and the system, and these simulated dialogues are used as training data. We also introduce a novel Audio Transfusion Forcing objective to train the AudioChat model, allowing it to simultaneously decompose high-level instructions via structured chain-of-thought reasoning and perform interactive multi-turn audio understanding/generation. To evaluate generation and editing performance, we develop three new metrics that directly measure task performance instead of relying upon distribution-based scoring. We highly encourage readers to visit our demo to better understand the capabilities of AudioChat: this https URL.

Somehow one can create multimodal embeddings from speech and text and make them useful. Some projects I've around recently: https://github.com/facebookresearch/SONAR Used for ASR WER approximation On the Robust Approximation of ASR Metrics Abdul Waheed, Hanin Atwany, Rita Singh, Bhiksha Raj https://arxiv.org/abs/2502.12408 Another one to detect dataset quality issues https://huggingface.co/yuriyvnv/WAVe-1B-Multimodal-PT

Audio Reasoning Challenge results https://audio-reasoning-challenge.github.io/leaderboard/ some info about winner Taltech entry https://www.linkedin.com/posts/aivo-olev-73944965_its-official-i-built-an-ai-agent-that-outperformed-ugcPost-7429801097202069504-G3U8 The task was to build an agent that can reason about audio using any open-source tools and my unique solution basically taught a deaf LLM (Kimi K2) to answer questions about 1000 audio files (music, speech, other sounds). That would be hard for a human as well. It had input from other LLMs and 35 tools that were able to pick up some unreliable info (ofter incorrect or even hallucinated) from the audio and that is what made this challenge the most exiting and why I basically worked non-stop for the 4 weeks. A normal AI agent can be pretty sure that when it reads a file or gets some other tool input that the information is correct. It might be irrelevant for the task, but mostly LLMs trust input (which is a problem in the real word with input from web search, malicious input, another agent's opinion etc). They also reason quite linearly which is a problem when you have unreliable info.

Very true https://x.com/KaitlynZhou/status/2023800965535789511 https://arxiv.org/abs/2602.12249 "Sorry, I Didn't Catch That": How Speech Models Miss What Matters Most Kaitlyn Zhou, Martijn Bartelds, Federico Bianchi, James Zou Despite speech recognition systems achieving low word error rates on standard benchmarks, they often fail on short, high-stakes utterances in real-world deployments. Here, we study this failure mode in a high-stakes task: the transcription of U.S. street names as spoken by U.S. participants. We evaluate 15 models from OpenAI, Deepgram, Google, and Microsoft on recordings from linguistically diverse U.S. speakers and find an average transcription error rate of 44%. We quantify the downstream impact of failed transcriptions by geographic locations and show that mis-transcriptions systematically cause errors for all speakers, but that routing distance errors are twice as large for non-English primary speakers compared to English primary speakers. To mitigate this harm, we introduce a synthetic data generation approach that produces diverse pronunciations of named entities using open-source text-to-speech models. Fine-tuning with less than 1,000 synthetic samples improves street name transcription accuracy by nearly 60% (relative to base models) for non-English primary speakers. Our results highlight a critical gap between benchmark performance and real-world reliability in speech systems and demonstrate a simple, scalable path to reducing high-stakes transcription errors.

8B TTS model claims to support many languages https://github.com/OpenMOSS/MOSS-TTS

https://github.com/FireRedTeam/FireRedASR2S Interesting things: FireRedVAD 100+ languages, 20+ Chinese dialects/accents* 🤗 | 🤖 FireRedLID 100+ languages, 20+ Chinese dialects/accents* 🤗 | 🤖 *FLEURS-VAD-102: We randomly selected ~100 audio files per language from FLEURS test set, resulting in 9,443 audio files with manually annotated binary VAD labels (speech=1, silence=0). This VAD testset will be open sourced (coming soon).

Some great results in phone recognition, no code yet but probably it will appear soon https://www.arxiv.org/abs/2602.01634 HuPER: A Human-Inspired Framework for Phonetic Perception Chenxu Guo, Jiachen Lian, Yisi Liu, Baihe Huang, Shriyaa Narayanan, Cheol Jun Cho, Gopala Anumanchipalli We propose HuPER, a human-inspired framework that models phonetic perception as adaptive inference over acoustic-phonetics evidence and linguistic knowledge. With only 100 hours of training data, HuPER achieves state-of-the-art phonetic error rates on five English benchmarks and strong zero-shot transfer to 95 unseen languages. HuPER is also the first framework to enable adaptive, multi-path phonetic perception under diverse acoustic conditions. All training data, models, and code are open-sourced. Code and demo avaliable at this https URL.

Low-resource ASR Leaderboard by Microsoft https://huggingface.co/spaces/microsoft/paza-bench

Interesting effort from Shinji on phoneme recognition https://huggingface.co/espnet/powsm https://arxiv.org/abs/2510.24992 POWSM: A Phonetic Open Whisper-Style Speech Foundation Model Chin-Jou Li, Kalvin Chang, Shikhar Bharadwaj, Eunjung Yeo, Kwanghee Choi, Jian Zhu, David Mortensen, Shinji Watanabe Recent advances in spoken language processing have led to substantial progress in phonetic tasks such as automatic speech recognition (ASR), phone recognition (PR), grapheme-to-phoneme conversion (G2P), and phoneme-to-grapheme conversion (P2G). Despite their conceptual similarity, these tasks have largely been studied in isolation, each relying on task-specific architectures and datasets. In this paper, we introduce POWSM (Phonetic Open Whisper-style Speech Model), the first unified framework capable of jointly performing multiple phone-related tasks. POWSM enables seamless conversion between audio, text (graphemes), and phones, opening up new possibilities for universal and low-resource speech processing. Our model outperforms or matches specialized PR models of similar size (Wav2Vec2Phoneme and ZIPA) while jointly supporting G2P, P2G, and ASR. Our training data, code and models are released to foster open science.

https://www.assemblyai.com/universal-3-pro new model by assembly ai, LLM based. Supposed to be free for February, so a good chance to test.