👾 Voice AI Showdown: STS vs. Pipeline Explained

The “AI part” of voice agents usually consists of three components:

1. A speech-to-text model that receives recorded audio from the user and converts it to text. You could call this step transcription.

2. The transcribed text is then processed by a large language model. The LLM generates a response text. E.g., user asks “where is my order?”. LLM looks up order information (tool use) and replies.

3. The LLM response is converted back to speech using a text-to-speech model. The audio is played.

An alternative, much simpler approach is using a speech-to-speech model. This basically combines all three steps into one. The model receives audio and responds with audio while doing the computation.

Stick with the proven STT→LLM→TTS pipeline or deploy speech-to-speech (STS) models? Here are some nuances you should consider:

Speech-to-Speech (STS) Models

Pros:

• Superior conversational flow - Handles interruptions, overlapping speech, and natural speech patterns apart from text (like a laugh)

• Emotional intelligence - Preserves tone, inflection, and vocal cues for more empathetic customer interactions

• Reduced latency - Direct audio processing is much faster because it eliminates multi-step delays

• Multilingual voice consistency - It can switch languages mid-sentence while maintaining speaker tone and pacing

Cons:

• Limited customization - At the moment, there are fewer voice options and minimal brand voice alignment compared to mature TTS libraries

• Restricted language support - Currently supports far fewer languages and regional accents than established TTS systems

• Vendor lock-in risk - End-to-end systems prevent swapping LLM components or TTS engines

• Deployment constraints - Often cloud-dependent with limited on-premise options for data-sensitive industries

STT + LLM + TTS Pipeline

Pros:

• Maximum flexibility - Swap OpenAI for Claude, change voice engines, or update STT models independently

• Comprehensive language coverage - 100+ languages with extensive dialect and accent support

• Enterprise-ready - Proven reliability with separate optimization of each component

• Voice customization - Extensive libraries for brand-aligned voice selection and fine-tuning

Cons:

• Higher latency - Multi-step processing creates noticeable delays in conversation flow

• Lost emotional context - Text intermediary strips away vocal nuance, resulting in less expressive responses

• Complex integration - Multiple vendors and APIs to manage, increasing technical debt

• Conversational deficits - Struggles with natural speech irregularities like hesitations or background noise

Conclusion

STS is great for consumer-facing applications that prioritize user experience. It also shines in use cases where emotional intelligence drives engagement and real-time applications that require fast response times.

The pipeline approach is better for deployments requiring specific LLM capabilities and multi-language applications with broad geographic coverage, extensive voice customization, or brand alignment. With a range of open-source solutions, it’s currently easier to meet strict data sovereignty requirements.

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