Speech-to-Speech vs Cascaded Voice AI: Which Architecture Should You Deploy?
Jan 28, 2026
S2S models promise 85% latency reduction and natural emotional expression. But enterprise adoption tells a different story. Here's the honest assessment of where speech-to-speech voice AI actually makes sense—and where traditional architectures still win.
What Is Speech-to-Speech (S2S) Voice AI?
Speech-to-speech (S2S) voice AI is an architecture where a single model processes audio input directly to audio output, eliminating the traditional text intermediary. Unlike cascaded voice AI platforms that chain STT → LLM → TTS, S2S models handle the complete audio-to-audio transformation in one step.
S2S promises significant latency reduction (85% in benchmarks) and better emotional prosody preservation since no information is lost converting speech to text and back. However, enterprise adoption remains limited due to control, debuggability, and compliance challenges that cascaded architectures handle better.
How Speech-to-Speech Voice AI Works
Speech-to-speech models burst onto the scene in 2025 with a compelling value proposition: collapse the entire voice AI pipeline into a single model.
Traditional cascaded voice AI architecture:
Audio In → STT → Text → LLM → Text → TTS → Audio Out
Each step adds latency. Each handoff loses information. The text intermediary strips emotional nuance from the input and struggles to add it back on output.
Speech-to-speech architecture:
Audio In → S2S Model → Audio Out
One model. Direct audio-to-audio processing. No text intermediary losing emotional context.
The results were genuinely impressive:
85% latency reduction: Response times dropped from 2000ms to 200-300ms
Emotional prosody preservation: Input emotions reflected in output tone
Natural conversation dynamics: Better interruption handling, more natural turn-taking
Major AI labs—OpenAI, Google, and others—invested heavily in S2S capabilities throughout 2025. The demos were stunning. The technology clearly worked.
So why isn't every enterprise deploying S2S in 2026?
Why Enterprises Still Choose Cascaded Voice AI Platforms
Here's what the production data actually shows:
Enterprise buyers still evaluate on resolution rates, handle time reduction, and containment metrics—not conversation naturalness.
The voice AI evaluation criteria shifted in 2025, but not toward "sounds more human." It shifted toward business outcomes: Does it resolve issues? Does it save money? Does it maintain compliance?
On these metrics, S2S models face challenges that traditional cascaded voice AI solutions don't.
The Control Problem
In a cascaded architecture, you have text intermediaries where you can:
Apply content filters before the customer hears anything
Check for compliance violations
Inject business logic based on conversation state
Log exactly what was said for audit trails
With S2S, there's no text intermediary. The model generates audio directly. By the time you could analyze what was said, the customer already heard it.
For regulated industries—healthcare, finance, legal—this is a dealbreaker. You cannot deploy a system where non-compliant content might reach a customer before you can review it.
The Debuggability Problem
When something goes wrong in a cascaded pipeline, you can isolate the failure:
Was the transcription wrong? → STT issue
Was the response inappropriate? → LLM issue
Did it sound robotic? → TTS issue
With S2S, you have one model producing audio. When output quality is poor, you can't easily determine why. Was it the model's understanding? Its response generation? Its audio synthesis? All three are fused together.
This makes systematic improvement extremely difficult. You can't fix what you can't diagnose.
The Fallback Problem
Cascaded architectures allow component-level redundancy:
If primary STT fails, fall back to secondary STT
If primary LLM is slow, route to faster model
If TTS has quality issues, swap providers
Each layer can have independent fallback mechanisms ensuring service continuity.
S2S is all-or-nothing. If the model fails, you fail. If the model is slow, you're slow. There's no component-level redundancy possible.
The Voice AI Evaluation Problem
The voice observability and AI agent evaluation ecosystem was built for cascaded architectures.
Existing tools know how to:
Evaluate transcription accuracy (WER, etc.)
Assess LLM response quality (relevance, accuracy, tone)
Measure TTS naturalness (MOS scores)
S2S requires entirely new evaluation approaches that assess the complete audio-to-audio transformation. These tools are immature compared to cascaded evaluation tooling.
4 Reasons Cascaded Voice AI Architecture Still Dominates
Given these challenges, traditional cascaded pipelines maintain enterprise dominance:
1. More Control
Text intermediaries enable:
Content filtering: Block inappropriate content before delivery
Compliance checks: Ensure regulatory requirements are met
Business logic injection: Modify responses based on customer state
Dynamic personalization: Insert customer-specific information
The text layer is where you implement business rules. Remove it, and you lose control.
2. More Fallbacks
Component-level redundancy means:
Provider outages don't take down the entire system
Quality issues in one layer can be isolated and addressed
You can swap components without rebuilding everything
Graceful degradation is possible at multiple levels
S2S architectures have a single point of failure.
3. More Debuggability
Layer isolation enables:
Root cause analysis of failures
Targeted improvements to specific components
A/B testing of individual layers
Clear attribution of quality issues
With S2S, debugging is "the model did something wrong"—not helpful for fixing it.
4. More Mature Voice Observability Tooling
The cascaded ecosystem includes:
Established AI agent evaluation frameworks
Proven voice observability systems
Compliance documentation patterns
Regulatory guidance for text-based AI
S2S tooling is still catching up. Deploying S2S means fewer tools, less guidance, and more unknowns.
Voice AI Platform Architecture: 2026 Adoption Predictions
Based on our research and conversations with industry leaders, here's how we expect S2S adoption to unfold:
H1 2026: Cascaded Voice AI Dominates
Expected S2S adoption: <15%
Market focus remains on scaling tool-using agents where traditional architecture offers:
Mature tool calling capabilities
Proven debugging approaches
Established compliance patterns
S2S will be used primarily for:
Internal experiments
Non-regulated, low-risk use cases
Premium support tiers where naturalness matters most
H2 2026: S2S Crosses Production Viability
Four enablers will unlock broader adoption:
1. Audio-native evaluation tools mature
New metrics designed for S2S quality assessment
Tools that don't require text intermediaries
2. Debugging capabilities catch up
Better interpretability of S2S model decisions
Audio-level attribution of quality issues
3. Compliance frameworks adapt
Regulatory guidance for audio-only AI systems
Post-hoc analysis tools that meet audit requirements
4. Cost parity achieved
S2S inference costs drop to match cascaded
Hardware optimization makes real-time S2S economical
Expected S2S adoption by end of H2: 25-30%
How to Choose: S2S vs Cascaded Voice AI Architecture
The right answer isn't "S2S everywhere" or "cascaded everywhere." It's matching architecture to use case requirements.
When to Deploy Speech-to-Speech Voice AI
Good S2S candidates:
Premium customer support tiers
Mental health and therapy applications
Coaching and personal development
Multilingual scenarios where emotional nuance matters
Luxury brand interactions where experience is primary
In these use cases, the naturalness advantages of S2S justify the control trade-offs.
When to Maintain Cascaded Voice AI Architecture
Keep cascaded architecture for:
High-volume tier-1 support (scale and cost matter most)
Regulated industries (healthcare, finance, legal)
Complex tool-calling workflows (need text for function calls)
Debt collection and compliance-heavy scenarios
Any use case requiring audit trails
In these use cases, control, debuggability, and compliance trump naturalness.
Voice Observability Requirements for Both Architectures
Regardless of architecture choice, you need:
Audio-native evaluation: Assess quality at the audio level
Emotion preservation metrics: Measure whether input emotions are reflected in output
Interruption handling measurement: Test natural conversation dynamics
End-to-end conversation quality: Evaluate complete interactions, not just components
If you can't measure it, you can't improve it—whether cascaded or S2S. This is where voice observability infrastructure becomes essential.
Voice AI Testing Strategy: The Practical Path Forward
Here's how forward-thinking teams are approaching S2S evaluation in 2026:
Phase 1: Optimize Cascaded Fundamentals (H1 2026)
Before experimenting with S2S, ensure your cascaded architecture is optimized:
Latency optimized across all components
Quality metrics established and tracked
Voice observability infrastructure in place
Baseline performance documented
You can't evaluate whether S2S is better if you don't know how good your current system is.
Phase 2: Controlled S2S Experiments (H2 2026)
Run S2S on limited traffic:
5-10% of calls for specific use cases
A/B test against cascaded for same scenarios
Measure quality, latency, cost, and customer satisfaction
Build intuition for where S2S excels
Phase 3: Selective S2S Deployment (2027)
Based on experimental data:
Deploy S2S where it demonstrably outperforms
Maintain cascaded where it still wins
Build hybrid routing that selects architecture per-call
The winners will run both architectures, optimized independently, routed intelligently.
Key Takeaways
S2S delivers real benefits. 85% latency reduction and emotional prosody preservation are genuine advances.
Enterprise adoption lags for good reasons. Control, debuggability, fallbacks, and mature tooling all favor cascaded architectures for production deployments.
H1 2026 belongs to cascaded. Less than 15% S2S adoption expected as enterprises focus on tool-using agents and compliance.
H2 2026 is the inflection point. Evaluation tools, debugging capabilities, and compliance frameworks will catch up.
Match architecture to use case. S2S for emotional connection; cascaded for reliability and compliance.
Invest in voice observability infrastructure. Whether cascaded or S2S, you need visibility to optimize.
Frequently Asked Questions About Speech-to-Speech Voice AI
What is the difference between S2S and cascaded voice AI?
Cascaded voice AI chains multiple models together: speech-to-text (STT) converts audio to text, an LLM generates a response, and text-to-speech (TTS) converts it back to audio. S2S uses a single model that processes audio input directly to audio output, eliminating the text intermediary. S2S is faster but harder to control and debug.
Is speech-to-speech voice AI faster than traditional architectures?
Yes, significantly. S2S achieves 85% latency reduction in benchmarks, with response times of 200-300ms compared to 2000ms for cascaded pipelines. This improvement comes from eliminating the multiple conversion steps and handoffs between models.
Why don't more enterprises use speech-to-speech voice AI?
Four main challenges limit enterprise S2S adoption: (1) lack of control—you can't filter content before it reaches customers, (2) debuggability—you can't isolate which part of the model caused problems, (3) no fallbacks—if the model fails, everything fails, and (4) immature evaluation tooling—voice observability tools were built for cascaded architectures.
Can speech-to-speech voice AI be used in regulated industries?
Currently, S2S faces significant challenges in regulated industries (healthcare, finance, legal) because there's no text intermediary where you can apply compliance checks before content reaches customers. For audit trails and regulatory compliance, cascaded architectures remain preferred. This may change as post-hoc analysis tools mature in H2 2026.
How do I evaluate speech-to-speech vs cascaded voice AI performance?
You need audio-native evaluation metrics that assess the complete transformation, not just individual components. Key metrics include emotional prosody preservation, interruption handling, end-to-end latency, and conversation completion rates. A/B testing both architectures on equivalent use cases provides the clearest comparison.
When will speech-to-speech voice AI be ready for enterprise deployment?
S2S is production-ready now for specific use cases: premium support tiers, non-regulated scenarios, and applications where emotional connection drives value. Broader enterprise adoption (25-30%) is expected by end of H2 2026 as evaluation tools, debugging capabilities, and compliance frameworks mature.
This article is based on findings from Coval's Voice AI 2026: The Year of Systematic Deployment report.
Evaluating S2S vs. cascaded architectures? Learn how Coval helps you test and compare voice AI approaches with voice observability and AI agent evaluation → Coval.dev