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.
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.