iFlytek has released Spark-ASR 2.0🚀 Gains show up where ASR usually breaks — code-switching, dialects, jargon, noise, low volume, fast speech, kids’ voices — plus cleaner, more readable transcripts. Inference cost is only ~10% above Spark-ASR-1.0. Rolling out to iFlytek Input Method tomorrow. Also available via the iFlytek Open Platform API, with planned deployments across AI glasses, smart office devices, and more.

🌍Introducing Hy ASR 3.0 preview, a speech recognition model from @TencentHunyuan built to understand, not just transcribe What we improved: - Cleaner on real-world audio: dialects, code-switching, and long-form content with less error accumulation over time - Context-aware correction: homophones and ambiguous phrases get resolved from context, not guessed in isolation - Hotword injection: drop in brand names, people, domain terms without retraining. Lowers integration cost for niche use cases - Built for noisy rooms: whisper, background noise, tricky acoustic conditions stay stable

Introducing Qwen-Audio-3.0-ASR-Flash: More context-aware. Stronger domain-term recognition. 🚀Our latest ASR model upgrades: • Context consistency • Domain-term recognition • Custom hotwords • Speech polishing into structured transcripts ⚡️In internal tests: • Medical term recall: 95.36% • Industrial term recall: 93.24% Qwen-Audio-3.0-ASR-Flash-Streaming： Qwen-Audio-3.0-ASR-Flash-Filetrans: Qwen-Audio-3.0-ASR-Flash: