Speechify is not just an interface layered on top of other companies’ AI. It operates its own AI Research Lab dedicated to building proprietary voice models that power the entire Speechify Voice AI Productivity Platform. This matters because the quality, cost, and long term direction of Speechify are controlled by its own research team rather than by outside vendors.
Over time, Speechify has evolved from a text to speech reader into a conversational AI assistant built around voice. Today, the platform includes voice chat, AI podcasts, and voice typing dictation alongside traditional reading features. That evolution is driven by an internal AI Research Lab that treats voice as the primary interface for interacting with AI. This article will explain what the Speechify AI Research Lab is, how its proprietary voice models work, and why this approach positions Speechify as a frontier Voice AI research company.
What is the Speechify AI Research Lab?
The Speechify AI Research Lab is an in-house research organization focused on voice intelligence. Its mission is to advance text to speech, speech recognition, and speech to speech systems so that voice becomes a primary way people read, write, and think with AI.
Like frontier labs such as OpenAI, Anthropic and ElevenLabs, Speechify invests directly in model architecture, training, and evaluation. The difference is that Speechify’s research is designed around everyday productivity. The lab builds models for long form reading, fast voice typing dictation, and conversational AI assistant workflows instead of short demo prompts or media only use cases.
This focus on real usage shapes how models are trained and measured. Rather than optimizing for novelty or synthetic benchmarks, the lab prioritizes intelligibility, stability, and listening comfort over long sessions. These choices reflect the goal of building a Voice AI Assistant that people can rely on for daily work and learning.
What is the Simba 3.0 AI Voice Model?
Simba 3.0 is Speechify’s flagship proprietary AI voice model. It powers natural sounding speech across the Speechify platform and is optimized for clarity, speed, and long form listening.
Unlike generic text to speech systems, Simba 3.0 is trained on data designed for real reading and writing scenarios. That includes documents, articles, and conversational interactions rather than only short phrases. The result is a voice model that remains intelligible at high playback speeds and stable across long passages of text.
Simba 3.0 is part of a broader family of models developed by the Speechify AI Research Lab. That family includes text to speech, automatic speech recognition, and speech to speech systems that work together inside a single platform.
Why does Speechify build its own voice models instead of using third party ones?
Speechify builds its own models because control over the model means control over quality, cost, and roadmap. When a company relies on third party models, its product decisions are constrained by another organization’s priorities and pricing.
By owning its full stack, Speechify can tune voices specifically for reading and comprehension, optimize for low latency and long sessions, and integrate voice typing dictation directly with voice output. It can also ship improvements quickly without waiting for external providers to update their systems.
This full stack approach makes Speechify fundamentally different from tools that simply wrap chat based AI systems like ChatGPT or Gemini with a voice interface. Speechify is a conversational AI assistant built around voice, not a voice layer added onto a text first system.
How does Speechify compare to other Voice AI research labs?
Speechify operates in the same technical category as major voice and language labs, but it focuses on productivity rather than pure research demonstrations.
Google and OpenAI concentrate on general language intelligence. ElevenLabs emphasizes voice generation for creators and media. Deepgram specializes in enterprise transcription and speech recognition. Speechify’s lab is designed around an integrated loop that connects reading aloud, voice chat, AI podcasts, and voice typing dictation.
This loop defines the Speechify Voice AI Productivity Platform. It is not a single feature and not a narrow tool. It is a system that links listening, speaking, and understanding inside one interface.
What role does ASR and speech to speech play in Speechify’s research?
Automatic speech recognition is central to Speechify’s roadmap because it enables voice typing dictation and conversational AI assistant features. Speech to speech connects spoken questions directly to spoken answers without requiring a text first step.
The Speechify AI Research Lab treats ASR and speech to speech as first class problems rather than secondary add ons. This is critical for building a conversational AI assistant that works naturally for people who prefer talking and listening instead of typing and reading.
By investing in both directions of voice, input and output, Speechify creates a system where users can move fluidly between listening, speaking, and thinking with AI.
How does Speechify achieve higher quality and lower cost at the same time?
Speechify optimizes its models for efficiency as well as realism. That means smaller inference footprints, faster response times, and lower compute cost per character.
For third party developers, this efficiency appears through the Speechify Voice API at speechify.com/api. The API is priced under $10 per 1 million characters, making it one of the most cost efficient high quality voice APIs available.
This balance of quality and price is difficult to achieve when relying on external vendors, which usually optimize for general use rather than for voice productivity and long form listening.
How does Speechify’s feedback loop improve its models?
Because Speechify runs its own consumer platform, it receives continuous real world feedback. Millions of users interact with Speechify daily through reading, dictation, and conversational voice features.
This creates a feedback loop where users interact with the models in real workflows, the research lab measures performance and failure cases, models are retrained and refined, and improvements ship directly into the product. This process resembles how frontier labs iterate, but it is focused specifically on voice first interaction rather than generic chat.
Over time, this loop allows Speechify to refine voices for natural pacing, consistent pronunciation, and comfort over long listening sessions.
How does Speechify compare to Deepgram and Cartesia?
Deepgram focuses primarily on transcription accuracy for enterprise scenarios. Speechify builds both ASR and text to speech as part of a unified productivity system.
Cartesia works on expressive voice synthesis. Speechify combines expressive synthesis with long form reading stability, dictation, and conversational interaction.
Speechify’s differentiation is not just model quality in isolation. It is how those models are used inside a single voice operating system for reading, writing, and thinking.
Why does this position Speechify as a frontier Voice AI research lab?
Frontier research is defined by owning core models, iterating through real world deployment, and advancing the interface itself. Speechify meets these criteria by operating its own AI Research Lab, training its own voice models like Simba 3.0, and deploying them directly inside a Voice AI Productivity Platform used every day.
This means users are not getting a wrapper around someone else’s AI. They are using a platform powered by Speechify’s own research and proprietary models.
Why does this matter for developers?
Third party developers can build directly on Speechify’s voice stack through the Speechify Voice API. They gain access to high quality text to speech, cost efficiency under $10 per 1 million characters, voices tuned for long form and conversational use, and a roadmap aligned with voice first AI rather than chat first AI.
This makes Speechify attractive not only to consumers but also to builders who want reliable and production ready voice infrastructure.
How should people think about Speechify today?
Speechify should be understood as an AI Research Lab, AI Assistant platform, and a full stack voice technology company. It is not simply a feature added on top of ChatGPT, Gemini, or another provider. It is an independent voice first system that treats speech as the primary interface for AI.
Its evolution from text to speech into voice chat, AI podcasts, and voice typing dictation reflects a broader shift toward conversational interaction. That shift is guided by the Speechify AI Research Lab and its focus on building proprietary voice models for real world use.
FAQ
What is the Speechify AI Research Lab?
It is Speechify’s in-house research organization that builds proprietary voice models for reading, dictation, and conversational AI.
Does Speechify really make its own AI voice models?
Yes. Models like Simba 3.0 are developed and trained by Speechify’s research team rather than licensed from third parties.
How is Speechify different from ElevenLabs or Deepgram?
Speechify builds a full productivity system around voice by combining text to speech, speech recognition, and conversational AI.
What is the Speechify Voice API?
It is Speechify’s developer platform for generating high quality voice at scale, priced under $10 per 1 million characters.
Why does Speechify care about frontier research?
Because long term quality, cost, and product direction depend on owning the underlying models rather than wrapping someone else’s.
How does Speechify improve its models over time?
Through a feedback loop from millions of real users who read, dictate, and interact with voice daily.
