ASR

ElevenLabs released Scribe v3 for recorded audio and Scribe v3 Live for realtime audio. The company claims 27% fewer word errors than Scribe v2 across 102 languages for v3, and 43% fewer errors than Scribe v2 Realtime for v3 Live, which also adds speaker diarization for the first time.

The Open ASR Leaderboard has added two private conversational English splits from Liva AI: Egyptian (1.34 hours) and Nigerian (1.55 hours). The Private tab will also report accent results using Kachru's Three Circles framework.

IBM released Granite Switch 4.1.1, a patch release that is fully backwards compatible with 4.1 and requires no migration. It updates the Granite Speech ASR model (10 languages) and the Decision adapter in granitelib-core, while the base model stays unchanged.

NVIDIA released Nemotron 3.5 Transcribe, an open-weight, promptable speech model that transcribes 40 languages, translates 31 to or from English and labels speakers in 13. It is available on Hugging Face and ModelScope alongside Granary v2 data and NeMo Speech fine-tuning recipes.

IBM released granite-speech-5.0-2b, a 2B-parameter speech-language model trained on 390,000 hours of public audio. IBM claims it ranks #2 among open-weight ASR models with a 5.06% average English WER. IBM also updated its 470M Turbo CTC model to 19,870 RTFx and refreshed the weights of granite-speech-4.1-2b-plus.

IBM has released Granite-Switch-4.1-8B and Granite-Switch-4.1-30B, models that bundle a Decision adapter, a Guardian safety adapter and a speech-to-text model behind a single endpoint. IBM says the combined model uses roughly the same GPU VRAM as the base model alone. Benchmark claims are IBM's own, and context window and pricing were not disclosed.

The Technology Innovation Institute (TII) released Falcon-ASR, a 1.6B-parameter speech recognition model focused on Arabic and the Emirati dialect. TII claims a 20.92% average word error rate across six Arabic test sets, versus 23.17% for the next-best system on the leaderboard snapshot it used. A demo is live on Hugging Face. Pricing and API availability have not been disclosed.

NVIDIA released Nemotron 3 Diarization, a 100-million-parameter open-weight model that identifies who is speaking and when in audio conversations. It ranked #1 among 17 system configurations on Voice Arena's Diarization-Bench with a 14.72% diarization error rate, supporting up to eight speakers in both live and recorded audio.

Microsoft Research has released VibeVoice-ASR-Streaming-7B, an open-weight streaming automatic speech recognition model that transcribes both who is speaking and what they say in real time. The model, listed at 9B parameters despite its name, supports 10 languages and custom hotwords under an MIT license.

IBM has released two new 470M-parameter Granite Speech models that transcribe audio at over 12,600x real-time speed on an NVIDIA H200 GPU. The encoder-only models achieve 4.85-5.00% word error rate while running over 20x faster than prior Granite Speech versions.

Microsoft Research released VibeVoice-ASR-BitNet, a quantized 1.58GB version of its VibeVoice-ASR speech recognition model that achieves real-time inference (RTF < 1) on as few as 3 CPU threads. The model runs 1.6-2.3x faster than Whisper.cpp on commodity x86 and ARM hardware, with a modest accuracy tradeoff.

ServiceNow released AU-Harness, the first comprehensive benchmark for code-switched speech recognition in enterprise voice agents, testing seven ASR systems including ElevenLabs, Gemini, and AssemblyAI. The benchmark covers 918 utterances across Spanish-English, French-English, Canadian French-English, and German-English, measuring Word Error Rate (WER), Semantic WER (SWER), and Answer Error Rate (AER). ElevenLabs Scribe V2 achieved the lowest WER across all language pairs, followed closely by AssemblyAI Universal-3 Pro.

NVIDIA released Nemotron 3.5 ASR, a 600M-parameter speech-to-text model supporting 40 language-locales from a single checkpoint. The model achieves 0.07 seconds to final transcript after speech ends and ranks 2nd in latency among streaming ASR models according to Artificial Analysis benchmarks.

IBM has released Granite Speech 4.1 2B, a 2-billion-parameter speech-language model trained on 174,000 hours of audio for automatic speech recognition and translation across English, French, German, Spanish, Portuguese, and Japanese. The model introduces a dual-head CTC encoder and includes variants for speaker attribution and a novel non-autoregressive architecture for higher throughput.