Beyond Recognition: Voices’ Checklist for Building Human-Centric AI Voice Models

After launching our exciting new Voice Data Product on July 17th, Voices is diving deep—not just into recognizing words, but into truly understanding what makes human communication tick. 

The future of AI voice isn’t just about hearing what we say, it’s about really getting us.

Blake Hayward, Senior Vice President of Product at Voices, explains the real challenge: Finding voice data that’s not just large in volume but rich, authentic, diverse, and trustworthy. 

If you missed the event, don’t worry; you can watch the full replay below.

Because honestly, AI trained on bland or biased data is like chatting with a parrot—it repeats words but completely misses the meaning behind them.

In this voice-first world, companies want to do more than just hear users—they want to connect. To do that, AI models need to evolve beyond turning speech into text; they must understand the context, emotions, and subtle meanings packed into human voices. 

Sounds ambitious? 

Blake broke it down with a five-point checklist—five essential ingredients that AI voice models must have to truly understand us.

The Voices Checklist: 5 Must-Haves for AI Voice Models That Really Understand You

1. Contextual Awareness
   AI can recognize words, but does it really get the bigger picture? For AI to truly understand people, it must interpret voice data in context—considering the setting, the ongoing conversation, and even the speaker’s environment. Without this, even perfectly recognized words can lead to hilarious or embarrassing misunderstandings.
   
2. Emotionally Expressive
   We don’t talk like machines—our voices carry emotions. A smart AI listens for tone, pitch, pace, and pauses, so it can tell if you’re frustrated, excited, or having an off day. This emotional savvy is vital for areas like customer service or mental health apps, where empathy makes all the difference.
   
3. Multi-modal Compatible
   Voice is key, but it’s only part of human communication. People also rely on gestures, facial expressions, and body language. For AI to really “read the room,” voice models should blend auditory data with visual cues, creating a richer understanding of user intent and feelings.
   
4. Bias Aware
   Not all voice data is created equal. To build fair, effective AI, training data must be collected and curated with an eye toward diversity—covering accents, languages, ages, and backgrounds. This prevents AI from struggling or showing bias toward any group, ensuring equitable experiences for all users.
   
5. Ethically Sourced
   High-quality data means nothing if it’s not gathered ethically. Data must be collected with informed consent, full transparency, and strict respect for privacy regulations. Ethical sourcing isn’t just a legal box to check—it builds trust and guarantees that AI technology develops responsibly, honoring every individual’s rights.
   

Behind the scenes, AI voice technology uses deep learning to simulate human speech from text or audio inputs. Components like Automatic Speech Recognition (ASR) capture sound, filter out noise, and process speech, converting it into text. Then neural networks learn from vast, real-world voice datasets to analyze and recreate natural speech patterns.

Text analysis plays a crucial role by breaking down words phonetically and analyzing context and pronunciation patterns, helping AI avoid the robotic, monotone speech of old.

AI voice generators let users input text or audio, tweak voice characteristics like tone and accent, and generate speech tailored to those parameters. Voice cloning and customization options allow for an even greater sense of authenticity, with choices around gender, accent, personality, and emotional expression.

These deep learning models don’t just run on generic data—they’re refined continuously using proprietary recordings from professional voice actors, which keeps the AI voices expressive, realistic, and uniquely human.

At Voices, we believe addressing these five critical needs is essential to building the next generation of AI voice models—ones that don’t just talk at us but truly talk with us. 

Our recent Voice Data Product launch is a testament to this commitment, providing high-quality, diverse, and ethically sourced voice data to power a future where AI voices understand and connect like never before.