IBM has released Granite Switch 4.1 in 8B and 30B sizes, a modular system that runs chat, decision-making and speech transcription from one hosted endpoint instead of separate models. The update adds a general-purpose Decision adapter, a specialized Guardian adapter for safety-related classifications, and an embedded ASR model, Granite-Speech-5.0-Turbo-CTC, which tops the OpenASR Leaderboard. Despite bundling three capabilities, the combined model uses roughly the same VRAM as the base model alone.
Anam, which builds photorealistic interactive avatars, switched to Deepgram's Flux speech-to-text model to solve turn-taking problems in unscripted, real-time conversation. In A/B testing against a self-hosted open-source STT option, Flux cut unwanted avatar barge-ins by roughly 50% with no added latency penalty, and complaints about turn-taking on English sessions dropped to near zero. Anam deployed Flux self-hosted on its own Kubernetes/H100 infrastructure, rolling it out to 100% of turnkey users after a two-week self-hosted deployment (versus days for Deepgram's hosted option).
Mistral AI has released Voxtral Mini 4B Realtime Arabic, a streaming speech-to-text model fine-tuned for Arabic dialects, Modern Standard Arabic, and code-switching between languages. The 4.4-billion-parameter model processes 16 kHz audio in real time with a configurable transcription delay, achieving an 8.82% average Character Error Rate across seven Arabic benchmarks at a 480ms delay — close to the offline Voxtral Transcribe Arabic model's 7.91% CER. It was co-developed with Morocco's digital transition ministry as part of a sovereign AI partnership and is released under Apache 2.0.
The Technology Innovation Institute (TII) has released Falcon-ASR, a 1.6 billion parameter automatic speech recognition model built primarily for Arabic, with particular focus on the Emirati dialect. It also transcribes English, French, Spanish and Portuguese using the same model weights. TII reports it beats the best published leaderboard result on a standard Arabic benchmark and leads on an internal Emirati evaluation, while also including word-level timestamps.
Deepgram has expanded its medical-specialized speech model, Nova-3 Medical, from English-only to 10 languages including Spanish, German, Hindi, Japanese and Russian, with automatic code switching in both batch and streaming modes. On a 115-hour curated medical audio test set, the multilingual medical model cut word error rate by 25-30% and entity error rate (drugs, doses, conditions) by 37-40% compared to Deepgram's general-purpose multilingual model. It also beat competing models from ElevenLabs and Microsoft on average word error rate across all 10 languages.
AssemblyAI introduced the Sync API, a new transport for transcribing short audio clips: one HTTP POST request returns a finished Universal-3.5 Pro transcript in the same response, at roughly 134ms median latency. It fills the gap between the Async API (submit-and-poll, 5-6 seconds of added latency) and the Realtime API (WebSocket, built for ongoing sessions), targeting workloads like dictation, voice-agent turn transcription, IVR, and push-to-talk. Clips from 80ms to 2 minutes and up to 40MB are supported, with word error rate of 1.59% on short-form audio. Pricing is $0.45/hr, the same rate as Universal-3.5 Pro Realtime.
AssemblyAI launched a hosted deployment of the open-source Qwen3.5 4B model on its LLM Gateway, optimized specifically for rewriting raw speech-to-text output into clean, formatted text. On voice rewrite tasks like dictation cleanup and transcript formatting, the model averaged 612ms response time—1.9x faster than GPT-4.1—at 94% lower cost per hour of audio processed. The model is priced at $0.10/$0.50 per million tokens (prompt/completion) and supports only max_tokens, temperature, and stream parameters, with no tool calling.
AssemblyAI released Universal-3.5 Pro, a new flagship async speech-to-text model priced at $0.21/hr. It natively transcribes code-switched conversations across 18 languages without separate configuration, introduces jointly modeled speaker diarization built directly into the transcript (rather than stitched from a separate system), and supports contextual prompting to prime the model with domain knowledge. Benchmarks show lower word error rate on code-switched audio and higher cpWER accuracy on diarization compared to several competitor models and AssemblyAI's own prior Universal-3 Pro.
NVIDIA released a detailed workflow for fine-tuning its Nemotron 3.5 ASR multilingual streaming model on Saudi Arabic dialects (Najdi and Hijazi). Using minimal data curation, replay mixing with FLEURS data, duration-based bucketing, and partial encoder unfreezing, they reduced word error rate on the target dialects from 55.05% to 29.96% while also slightly improving English and other Arabic performance. The post also covers decoding tweaks and speaker diarization extensions for multi-speaker transcription.