Speech recognition

Oct 01, 2026

How a Voice Agent Learns the Rhythm of Conversation — Shawn Wen

Shawn, CTO of PolyAI, explains why voice agents are harder than text agents: voice adds time, and good conversation depends on adapting to the person on the line. He describes Dialog-RSN-1, an audio-native model that predicts turn-taking, replies in text with citations, and writes the transcript last for auditability, along with training on real noisy calls, latency and what agents should do while thinking, voice personality, benchmarking, harness ownership, and the shifting bottleneck from producing content to validating it.

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.

Jul 25, 2026

Why Large? Tiny LMs & Agents on Edge/Robotics — Cormac Brick, Google

Cormac Brick from Google AI Edge discusses how the increasing constraint of DRAM cost on edge devices necessitates the development and deployment of increasingly smaller AI models. He outlines the work of his team in optimizing models like Gemma, achieving 2.9 bits per weight for a 2 billion parameter model capable of running on a Raspberry Pi at 7.6 tokens/second, or on an NPU at 31 tokens/second decode for vision tasks. The talk delves into 'tiny models' (50M-500M parameters) that extend AI to older devices and enable features like robust voice-to-function calling via fine-tuning with synthetic data, exemplified by an offline voice dictation app.

Jul 08, 2026

The next generation of ChatGPT Voice

An in-depth look into GPT Live, the next generation of full-duplex voice models in ChatGPT, designed for natural, continuous interaction. It highlights breakthroughs in concurrent processing, intelligent delegation to advanced models like GPT 5.5, real-time semantic translation, and proactive language coaching, aiming to transform AI interactions into fluid, intelligent conversations akin to human dialogue.

Nov 13, 2025

Building Voice Agents Just Got Easier

Anoop Dawar from Deepgram discusses the evolution of voice AI, from basic transcription to sophisticated, real-time voice agents. He covers the key technical challenges in production, such as latency and interruption handling, and introduces Deepgram's Flux system. The talk concludes with a look at the future of speech-to-speech models that can understand emotional nuance, moving closer to passing the audio Turing Test.

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.