Deepgram’s June 2026 Self-Hosted release (260611) introduces Persian profanity filtering, English redaction for Flux streaming, a streaming diarization model parameter, and Text-to-Speech transcoding.…
Deepgram introduced reusable agent configurations via its API, allowing users to store and reference agent setups by UUID instead of resending full configurations per session. This supports per-custom…
Deepgram’s Voice Agent API now supports NVIDIA as an LLM provider, offering the `nemotron-3-nano-30B-A3B` model in the Standard pricing tier. The integration simplifies agent configuration by allowing…
Deepgram’s Voice Agent API now includes an optional `thought_signature` field to improve function calling performance for Gemini 3.0/3.1 models, addressing prior degradation. It also adds a `volume` p…
Deepgram’s Nova-3 model now supports Simplified and Traditional Chinese (language codes `zh`, `zh-CN`, `zh-Hans`, `zh-TW`, `zh-Hant`). Users can access these via the `model="nova-3"` parameter in API …
Deepgram introduced Batch Diarization V2, a major upgrade to its speaker labeling system for pre-recorded audio, improving attribution accuracy and reducing errors by up to 3.3X in human evaluations. …
Deepgram’s DACH team will host a technical webinar on June 25, 2026, demonstrating how to develop production-ready voice agents for German enterprise deployments. The session will cover real-time spee…
Deepgram highlights how accent variability in ASR layers undermines multi-region restaurant voice ordering accuracy, citing McDonald's pilot failures. It introduces techniques like Keyterm Prompting (…
Deepgram introduced Deepgram for Restaurants, a voice AI platform designed for enterprise-scale restaurant brands with complex menus and POS systems. The solution provides custom fine-tuning, noise fi…
Proposed HIPAA Security Rule updates (effective 2026) and voice AI adoption are creating gaps in existing Business Associate Agreements (BAAs). Real-time audio processing, transcription, and subcontra…
Deepgram’s article highlights that NER models lose 20–27 F1 points on raw ASR output due to formatting, casing, and error cascades. It compares pipeline, LLM, and joint-model architectures, emphasizin…
Deepgram’s guide outlines two approaches to integrate voice agents with Salesforce: native Agentforce Voice or external STT APIs via Salesforce’s Telephony Integration API. The article emphasizes the …
Deepgram published a production playbook for AI call center voice agents, detailing latency budgets, failure modes, cost modeling, and monitoring metrics. It highlights the orchestration layer as the …
Deepgram partnered with Fortanix and NVIDIA to enable enterprises to run voice AI in private environments using Fortanix Confidential AI and NVIDIA Confidential Computing. This integration protects se…
Deepgram released an upgraded Nova-3 Medical batch model with expanded medical vocabulary and improved medical term recognition (97.20% KRR). The update maintains word error rate parity and is availab…
Deepgram’s guide compares Cascade and Speech-to-Speech (S2S) voice agent architectures, emphasizing tradeoffs in cost, debuggability, and compliance. Cascade pipelines expose text at each stage, aidin…
Deepgram’s product marketing manager argues that dynamic range compression (DRC) is often unnecessary for voice AI pipelines and can degrade transcription accuracy. The article provides a decision fra…
Deepgram’s article highlights how health systems deploy AI voice agents for scheduling, refills, and triage, emphasizing the critical role of the speech-to-text (STT) layer in production success. It d…
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