Deepgram Achieves Key Milestone in Delivering a Speech-to-Speech Architecture

Deepgram has developed a speech-to-speech model that operates without text conversion at any stage, enabling fully natural and responsive voice interactions that preserve nuances, intonation, and emotional tone throughout real-time communication.

Deepgram is transforming speech-to-speech modeling with a new architecture that fuses the latent spaces of specialized components, eliminating the need for text conversion between them. By embedding speech directly into a latent space, Deepgram ensures that important characteristics such as intonation, pacing, and situational and emotional context are preserved throughout the entire process.

What sets Deepgram apart is its approach to fusing the hidden states—the internal representations that capture meaning, context, and structure—of each individual function: speech-to-text (STT), large language model (LLM), and text-to-speech (TTS). This fusion is the first step toward training a controllable single, end-to-end speech model, enabling seamless processing while retaining the strengths of each component.

"This achievement represents a fundamental shift in how AI systems can process and respond to human speech," said Scott Stephenson, CEO and co-founder of Deepgram, in a statement. "By eliminating text as an intermediate step, we're preserving crucial elements of communication and maintaining the precise control that enterprises need for mission-critical applications."

Key benefits of Deepgram's new architecture include the following:

• Optimized latency design;
• Enhanced naturalness, preserving emotional context and conversational nuances;
• Native ability to handle complex, multi-turn conversations;
• Unified, end-to-end training across the entire model, creating a more cohesive and inherently adaptive system that fine-tunes its understanding and response generation directly in the audio space.

This architecture also allows developers to inspect and understand how the system processes spoken dialogue. The design incorporates speech modeling of perception, natural language understanding/generation, and speech production, preserving distinct capabilities during training. Through the ability to decode intermediate representations back to text at specific points, developers can gain insight into what the model perceives, thinks, and generates, ensuring its internal representation aligns with the model output and stays true to the intent of the business user, addressing hallucination concern in scaled business use cases. This capability allows the user to peer into each step throughout generation, helping refine models, improve performance, and deliver more accurate, lifelike, and reliable speech-to-speech solutions.

Once Deepgram's end-to-end STS model moves to production, businesses will be able to adopt this breakthrough directly through its voice agent API from within the current Deepgram platform. Deepgram's platform includes capabilities such as the following:

• Adaptability to dynamically fine-tune models for specific industry language, ensuring high accuracy across diverse applications without constant retraining.
• Automation to streamline transcription, model updates, and data processing.
• Synthetic data generation to improve model training, even with limited real-world data.
• Data curation to clean, manage, and organize training data.
• Model hot-swapping to switch between models to optimize performance for specific tasks.
• Integrations between Deepgram's voice AI with cloud platforms, enterprise systems, and third-party applications, embedding it within existing workflows.