Sep 15, 2026

Realtime Voice Agents with Frontier Intelligence — Bohan Li, EliseAI

Bohan Li of EliseAI unveils a sophisticated architecture for real-time voice agents that merges high intelligence with low latency, akin to a self-driving car's perception, planning, and control systems. Key innovations include a speculative transcriber combining fast streaming with accurate contextual correction, background agents for offloading LLM tool calls and enabling eager generation, and a clever prefix cache for speech synthesis that seamlessly blends pre-cached audio with dynamically generated speech to mask underlying processing delays.

Sep 14, 2026

When Talking Becomes the Main Way We Use Computers — Pavan Muddireddy

Mistral AI's Voxtral suite represents a significant advancement in voice AI, moving beyond traditional cascaded systems towards more integrated and efficient audio understanding and generation. Pavankumar Reddy Muddireddy of Mistral AI details Voxtral Chat's audio-native architecture, which directly feeds continuous audio embeddings into a text LLM for rich context. He explains Voxtral TTS's use of continuous latents and flow matching for high-fidelity speech synthesis. The discussion also covers critical challenges like speaker diarization, the use of Direct Preference Optimization (DPO) to correct model hallucinations, and the enduring relevance of specialized model cascades in real-world enterprise deployments for their adaptability and control, emphasizing that voice typically thrives alongside a screen, not in isolation.

Aug 25, 2026

⏭️ Forward Deployed: Voice AI on what works in 2026

This podcast episode delves into the pragmatic realities of building and deploying voice agents in enterprise. Featuring leaders from Decagon, Vapi, Retell, Daily, and Smallest AI, the discussion uncovers why the current state-of-the-art relies on cascaded pipelines (STT -> LLM -> TTS) instead of direct voice-to-voice models. Key challenges explored include managing latency versus intelligence, ensuring system reliability through fallback models, the complexities of turn-taking, and architectural strategies to overcome LLM context limitations and optimize costs. The experts also touch upon the differences between inbound and outbound use cases, multilingual considerations, and the potential future of hybrid voice agent architectures.

May 09, 2026

Why TTS Models Now Look Like LLMs — Samuel Humeau, Mistral

Samuel Humeau from Mistral explains the dominant architecture for modern text-to-speech (TTS) systems, which mirrors large language models. He details how neural audio codecs solve the information density problem, the autoregressive transformer backbone for generation, and the streaming techniques used to achieve low perceived latency in voice agents. The talk uses Mistral's open-weight TTS model as a practical example.

Nov 04, 2025

ElevenLabs CEO: Why Voice is the Next AI Interface

Mati Staniszewski, CEO of ElevenLabs, discusses the company's strategy for rapidly shipping research-grade AI. He covers their organizational structure of small, autonomous teams, a global and remote-first hiring philosophy, the transition from a creator-focused product to an enterprise platform, and the lessons learned in navigating complex licensing and scaling a go-to-market team.

Oct 17, 2025

Distant conversational speech recognition: Challenges and Opportunities

Dr. Samuele Cornell from Carnegie Mellon University discusses the persistent challenges in distant automatic speech recognition (DASR) for spontaneous, multi-party conversations. He explains why state-of-the-art systems falter in real-world scenarios and presents recent advancements through three key efforts: (1) insights from the CHiME-7/8 DASR challenges, which benchmark robust meeting transcription; (2) progress towards unified end-to-end models that jointly handle diarization and recognition; and (3) novel techniques for generating realistic, large-scale training data using a combination of large language models and multi-speaker text-to-speech systems.