Voice Activation Technology

Imagine trying to get a workout recommendation while running, navigate a complex route while driving, or get tech support while cooking - all without touching a screen. This is the promise of voice-enabled LLM agents, a technological leap that's redefining how we interact with machines. Traditional text-based chatbots are like trying to dance with two left feet. They're clunky, impersonal, and frustratingly limited. Consider these real-world friction points: - A visually impaired user struggling to type support queries - A fitness enthusiast unable to get real-time guidance mid-workout - A busy professional multitasking who can't pause to type a complex question Voice AI breaks these barriers, mimicking how humans have communicated for millennia. We learn to speak by four months, but writing takes years - testament to speech's fundamental naturalness. Real-World Transformation Examples: 1️⃣ Healthcare: Emotion-recognizing AI can detect patient stress levels through voice modulation, enabling more empathetic remote consultations. 2️⃣ Fitness: Hands-free coaching that adapts workout intensity based on your breathing and vocal energy. 3️⃣ Customer Service: Intelligent voice systems that understand context, emotional undertones, and personalize responses in real-time. The magic of voice lies in its nuanced communication: - Tone reveals emotional landscapes - Intensity signals urgency or excitement - Rhythm creates conversational flow - Inflection adds layers of meaning beyond mere words - Recognize emotional states with unprecedented accuracy - Support rich, multimodal interactions combining voice, visuals, and context - Differentiate speakers in complex conversations - Extract subtle contextual intentions - Provide personalized responses based on voice characteristics In short, this technology is about creating more human-centric technology that listens, understands, and responds like a thoughtful companion. The future of AI isn't about machines talking at us, but talking with us.

A new generation of customer-service voice bots is here, spurred by advances in artificial intelligence and a flood of cash, Belle L. reports. Insurance marketplace eHealth, Inc. uses AI voice agents to handle its initial screening for potential customers when its human staff can’t keep up with call volume, as well as after hours. The company slowly became more comfortable with using AI voice agents as the underlying technology improved, said Ketan Babaria, chief digital officer at eHealth. “Suddenly, we noticed these agents become very humanlike,” Babaria said. “It’s getting to a point where our customers are not able to differentiate between the two.” The transition is happening faster than many expected. “You have AI voice agents that you can interrupt, that proactively make logical suggestions, and there’s very little or no latency in the conversation. That’s a change that I thought was going to happen a year and a half or two years from now,” said Tom Coshow, an analyst at market research and information-technology consulting firm Gartner. Venture capital investment in voice AI startups increased from $315 million in 2022 to $2.1 billion in 2024, according to data from CB Insights. Some leading AI models for voice applications come from AI labs like OpenAI and Anthropic, startup founders and venture capitalists say, as well as smaller players like Deepgram and Assembly AI, which have improved their speech-to-text or text-to-speech models over the past few years. For instance, OpenAI’s Whisper model is a dedicated speech-to-text model, and its GPT-4o model can interact with people by voice in real-time.

Prediction: AI Voice agents will be the fastest-growing part of the healthcare workforce in 2025. Imagine a nurse stepping into her car after completing a start-of-care visit aided by an AI ambient listening app. With a quick push of a button, she receives a phone call from her clinical assistant agent. “I’ve analyzed the recorded audio from your visit with Jane Smith and completed 60% of the OASIS document. Would you like to discuss the areas needing more information?” With the visit fresh in her mind, the nurse effortlessly completes her documentation through a natural conversation, eliminating manual entry into the EMR. Her AI assistant also prepares her for the next patient, providing necessary details and saving valuable time. This is the promising future for healthcare AI voice agents. As opposed to the frustrating experience of IVR systems, recent advancements in capabilities and responsiveness have dramatically improved Voice AI technology, finally allowing an AI agent to carry a natural conversation much in the way humans would. These agents, operating on behalf of healthcare providers, are set to transform routine communications in healthcare, significantly reducing operational costs and boosting productivity while improving patient experience. Key areas of immediate impact are likely to be the following: Patient Scheduling & Visit Confirmation: AI voice agents can proactively confirm appointments, reducing costly no-shows by providing convenient rescheduling options if needed. Patient Engagement, Education & Care Management: With virtually limitless capacity, voice agents can frequently engage with patients, driving medication adherence, monitoring conditions, and offering personalized health education at scale. Referral Intake & Coordination: AI agents streamline the referral process by instantly capturing and verifying patient details, coordinating seamlessly with referring providers, and quickly updating clinical teams—accelerating patient onboarding and care delivery. Authorizations and Billing: Voice assistants automate verification of insurance coverage, obtain prior authorizations swiftly, and address common billing inquiries efficiently, significantly reducing administrative workloads. Caregiver Recruiting & Retention: AI-driven initial candidate screenings, qualification checks, and timely follow-ups enhance recruitment efficiency and candidate experience, allowing agencies to attract and retain caregivers effectively. What would you do with an unlimited AI voice agent workforce?

Voice has emerged as a widely adopted modality in applied AI. Yet the technical execution is difficult to get right. In my latest post, I dive into the technical architecture of building voice agents. This enables us at Greylock to better empathize with engineering challenges, assess product depth with greater rigor, and stay ahead of shifts in the voice stack. The post explores how teams are bringing voice agents into production across three distinct layers of the stack, breaks down the core technical challenges of building voice agents, and also highlights where we see enduring infrastructure needs across the voice stack. Here are the highlights: 1/ Layers of the stack: Teams deploying voice agents in production typically operate across one of three layers of the voice stack - core infrastructure, frameworks and developer platforms, and end-to-end applications. Each comes with its own engineering tradeoffs and product considerations. 2/ Under the hood: Today, most production-grade voice systems follow a three-part architecture: (1) a speech-to-text (STT) model, (2) a large language model (LLM), and (3) a text-to-speech (TTS) model. An emerging alternative is using end-to-end speech-to-speech (S2S) models, which would skip the intermediate transformations from audio to text and then back to audio. These models are generally more expressive and conversational out-of-the-box but not yet ready for most production use cases. 3/ Key technical considerations: Regardless of the architecture, delivering high-quality voice interaction requires solving problems across the entire stack such as:  - Latency - Function Calling Orchestration - Hallucinations and Guardrails - Interrupts and Pauses - Speech Nuances - Background Noise and Multi-speaker Detection 4/ Foundational infrastructure: In our conversations with both builders and buyers, we repeatedly heard that reliability, quality, security, and compliance are critical gating factors for production deployment. Builders need confidence that the agents they’re deploying will perform reliably across edge cases, while buyers look for tools to evaluate and monitor agent behavior in real-world environments.  We’re excited to continue learning from teams working at the forefront of voice infrastructure and agentic voice applications. If you're building in this space, we would love to connect. Thanks to Timothy Luong, Dave Smith, Catheryn Li, Andrew Chen, Ben Wiley, Arno Gau, Jason Risch, Jerry Chen, Corinne Marie Riley, and Christine Kim for their thought partnership. Read the essay here: https://lnkd.in/g63MNZg6

🗣️ Voice AI is everywhere—but which use cases are delivering ROI today, and which will tomorrow? We map adoption into four waves—full breakdown here 👉 https://lnkd.in/gbxveFjA Voice AI’s “second act” isn’t a gimmick; it’s becoming the backbone of autonomous workflows in trillion-dollar industries. Voice isn’t just the product anymore—what matters is what voice unlocks across an entire organization. 1️⃣ Wave 1 — Infrastructure Foundational models, tooling, and orchestration. Cartesia Vapi LiveKit Hamming AI David AI etc. 2️⃣ Wave 2 — Horizontal Platforms 24/7 AI call-center agents replacing legacy phone trees. Here, a high quality voice agent from Cresta, Parloa, Sierra, or Decagon is still quite central to “the product.” 3️⃣ Wave 3 — Vertical Agents (now) Domain-specific agents eating labor spend, in which voice is either a wedge or expansion exponent. 📦 Logistics: Augment, HappyRobot, FleetWorks, Vooma, and Pallet each automate different parts of the Freight Forwarder, Broker, Carrier, and Shipper stack such as load updates, scheduling, and carrier negotiations—chipping away at legacy TMS/WMS. 🛡️ Insurance: Strada & Liberate integrate with Guidewire / Applied to run sales and service 24/7. 🩺 Healthcare & Pharma: Assort Health & Hippocratic AI book visits, triages calls, and guides patients with empathy. Tandem & Squad Health address cumbersome processes like prior authorizations, financial assistance management, and pharmacy coordination. 🏭 Manufacturing & Wholesale Distribution: Endeavor & Canals AI ingest multi-channel orders, sync ERPs, and surface cross-sell insights. DOSS.COM built an ERP to unify inventory, orders, and production into one platform. 🛠️ Home Services: Netic & Avoca layer AI agents on intake, scheduling, and quoting for trades pros. 🔍 User Research: Listen Labs & Strella deliver adaptive voice interviews at survey speed, replacing weeks of moderated sessions. 4️⃣ Wave 4 — Edge-Native, Trust-First Companions (emerging) Consumer adoption has lagged, but NPUs now ship in every phone, laptop, and wearable—running billion-parameter speech models fully offline. Qualcomm’s AR1+ glasses, Snapdragon X PCs, and Google’s Gemini Nano prove it: sub-second, privacy-safe voice on the edge. Add “dialect packs” that load on demand, plus FCC & EU rules that watermark every utterance, and the stage is set for ambient AI sidekicks that feel personal and compliant. Resonant personalities are the secret ingredient for consumer, enabled by culturally nuanced voices users choose to spend time with. Voice AI isn’t just here to stay—it’s opening the floodgates for the complete transformation of countless verticals and consumer applications. If you're building in any of these areas, please reach out to Kristina Shen and I—we'd love to chat!

I just had a conversation with an AI voice assistant that sounded completely human. Voice AI is crossing the "uncanny valley" and changing how we interact with technology. On the latest #GenerationAI podcast, my co-host JC and I explored Sesame - an open-source voice model that's making waves. We tested it live on the show and the results were mind-blowing. The breathing patterns, emotional range, and natural pauses felt eerily real. This isn't your clunky old Siri or Alexa anymore. These new voice agents will transform how colleges handle student support. Think about it: 24/7 financial aid help without the two-hour wait times. Multilingual support for international students and their families. No more press-1-for-this phone trees or frustrating call routing. Schools are already implementing this tech, with some handling 30-50% of calls via AI. Voice is becoming our primary way to interact with AI, not text prompts. Want to hear what the future sounds like? Check out the full episode where we demo the technology and break down what it means for higher education. The robots don't sound like robots anymore - and that changes everything.

Let’s talk about voice AI. Meta's recent PlayAI acquisition is just the beginning of a wave of voice AI consolidation driven by big tech’s uncapped appetite for the building blocks for the AI future. Meta’s play isn't just about technology or talent; it positions Meta to lead in the integration of voice AI as a dominant interface for AI interaction. As the industry continues to surpass critical thresholds in models that process audio directly, the top voice AI development startups are building platforms that enable easy integration of sub-300ms voice capabilities without complex infrastructure. The top voice AI development platform companies and leading M&A targets include: → ElevenLabs represents the crown jewel of the voice AI space. Its market-topping 955 Mosaic score and voice synthesis leadership make it the most attractive acquisition target for the big tech companies with “money to blow” on AI-cquisitions. → Cresta offers proven ROI with customers reporting 50% cost reductions in contact centers. This positions it perfectly for companies looking to leverage voice AI to immediately impact enterprise productivity. → Cartesia brings ultra-low latency capabilities under 100ms, making it ideal for any company seeking to deliver truly conversational AI experiences. Voice AI investment and consolidation reflects a future where human-AI interaction will be conversational. Companies are positioning for a future where humans interact with AI away from the browser or mobile paradigm we have existed in for the last two decades. Voice AI is becoming the "plumbing" of real-world AI interaction across both consumer and enterprise applications. As the AI arms race continues, acquisitions will continue to be focused on talent, tech, and infrastructure rather than existing revenues. Companies that secure advanced voice AI capabilities now will dominate the next phase of AI adoption – whether they integrate into their existing offerings or cash-in on selling the tooling back to others. The voice AI gold rush isn't just about near-term tech boosts; it's about defining how humans will interact with AI for the next decade. P.S. Want more insights on the companies building the future of voice AI? Have your AI talk to my AI... or, comment "voice AI" below for *free* access to CB Insights' data and insights on the voice AI markets.

 Is This the Future of Human-AI Interaction? Sesame's "Voice Presence" is Astonishing. Have you ever truly felt like you were having a conversation with an AI? Sesame, founded by Oculus co-founder Brendan Iribe, is pushing the boundaries of AI voice technology with its Conversational Speech Model (CSM). The results are striking. As The Verge's Sean Hollister noted, it's "the first voice assistant I've ever wanted to talk to more than once." Why? Because Sesame focuses on "voice presence," creating spoken interactions that feel genuinely real and understood. What's the potential impact for businesses? Enhanced Customer Service: Imagine AI assistants that can handle complex inquiries with empathy and natural conversation flow. Improved Accessibility: More natural voice interfaces can make technology accessible to more users. Revolutionized Content Creation: Voice models like Maya and Miles could open up new audio and video content possibilities. Training and Education: Interactive AI tutors could provide personalized and engaging learning experiences. The most impressive part? In blind listening tests, humans often couldn't distinguish Sesame's AI from real human recordings. #AI #ArtificialIntelligence #VoiceTechnology #Innovation #FutureofWork #CustomerExperience #MachineLearning #SesameAI

Tired of typing to your AI? Voice AI is the next big shift. Typing can be tedious and often not convenient at all (especially when multitasking). There are also voice-native tasks, like customer service or sales calls, that benefit from AI systems that can understand voices. That’s why Voice AI is heating up 🔥 With strong voice systems, you can simply speak directly to your LLM or AI agent. And it can speak back! Most Voice AI systems today combine multiple models working together in three steps: 𝗦𝗽𝗲𝗲𝗰𝗵-𝘁𝗼-𝗧𝗲𝘅𝘁: converts the user’s audio into text 𝗟𝗟𝗠 (𝘁𝗵𝗲 “𝗯𝗿𝗮𝗶𝗻”): reasons and generates a text response 𝗧𝗲𝘅𝘁-𝘁𝗼-𝗦𝗽𝗲𝗲𝗰𝗵: turns that text into realistic speech Voice AI isn’t without challenges. Voice data is messy. Imagine phone calls or voice notes. There can be: • Background noise • Multiple speakers • Low-quality microphones • Unstable internet connection Still, the potential is huge. And we’re seeing rapid progress in low-latency voice models, evaluation methods, and multimodal integration. Voice is quickly becoming the next interface for AI. Soon, talking to your AI will feel as natural as talking to a person. Notes and resources in the comments to learn more. ⬇️ -- 🔄 Repost to share the joy of talking to your AI. 👋🏽 I’m Shelby, and I write about deploying efficient, cutting-edge AI.