From ASR to CSR: Why Conversation Changes Everything

For decades, Automatic Speech Recognition (ASR) has been the backbone of voice technology. It powers transcription services, live captions, dictation tools, and countless applications where accuracy matters but conversational context and turn-taking do not.

But if you have ever tried to build a real-time voice agent, you know: transcription is not conversation.

The challenge is not just recognizing words. It is managing the flow of dialogue. Natural conversations involve overlapping speech, pauses that do not always mean “I am done,” and interruptions that need to be handled gracefully. Whether your agent cuts in too soon or waits too long, the interaction feels robotic instead of human.

ASR was never built for that.

This is why we need a new category: Conversational Speech Recognition (CSR).

What Is Conversational Speech Recognition (CSR)?

Conversational Speech Recognition refers to a class of speech-to-text systems designed from the ground up for real, interactive dialogue.

Where ASR listens passively and produces transcripts, CSR behaves more like a participant in a conversation. It not only recognizes what was said, but also provides the signals necessary to know when a speaker has finished, when they have resumed, and when it is appropriate for an agent to respond.

In other words: ASR is like a stenographer, focused on recording. CSR is closer to a conversation partner, aware of timing, context, and dialogue flow.

Why ASR Falls Short in Conversational Use Cases

Developers building voice AI today have pushed ASR far beyond its original design. The typical recipe looks like this:

• Use ASR to stream transcripts.

• Layer on Voice Activity Detection (VAD) to spot silences.

• Add custom rules or turn-taking models to decide when to trigger responses.

This approach works up to a point, but it introduces several problems:

• Premature cut-offs. Short thresholds cause the agent to jump in too soon, clipping the user’s words.

• Robotic pauses. Long thresholds cause awkward delays, making the agent feel slow and unresponsive.

• Client-side complexity. Developers are forced to write orchestration code to juggle multiple components, thresholds, and partial transcripts, instead of focusing on building the agent.

Even with these add-ons, the system is not truly conversation-aware. Instead, it often feels less like a natural dialogue and more like using a walkie-talkie, where you wait for the other person to say “over” before taking your turn. That stop-and-go rhythm might be fine for radios, but it breaks the flow of real conversation.

Core Capabilities of CSR

CSR systems address these challenges by treating conversational flow as part of recognition itself. At a high level, CSR introduces several technical capabilities that set it apart from ASR:

• Turn-Aware Transcripts. CSR delivers text aligned to natural conversational boundaries, reducing the need for manual stitching of fragments.

• Contextual Turn Detection. Instead of relying only on silence, CSR uses acoustic and linguistic cues to determine when a speaker has finished.

• Streaming-First, Low Latency. CSR is engineered for interactive applications where responses need to happen within hundreds of milliseconds, not seconds.

• Conversational Cues. Beyond transcripts, CSR can provide signals about conversational states, for example when speech starts, pauses, or resumes, giving developers the hooks they need to manage dialogue flow.

• Configurable Tradeoffs. CSR systems expose controls that let developers balance responsiveness, accuracy, and cost according to their use case.

• Stateful Within Turns. CSR maintains context during a turn, which keeps transcripts coherent and avoids issues like dropped partials or inconsistent outputs.

• Robustness to Real Speech. CSR is trained to handle fillers like “uh” or “you know,” restarts, and the messy, informal nature of live dialogue.

CSR vs. ASR: A Technical Comparison

Here is how CSR compares to ASR across some of the capabilities developers care about most for voice agent development:

Why CSR Matters Now

The voice AI landscape is shifting.

What were once demos and proof-of-concept voice bots are now moving into production as customer service agents, healthcare assistants, sales agents, and real-time support tools. The voice AI market is moving toward truly interactive use cases, but the legacy technology stack was never designed for this.

In these settings, it is not just about latency. A clipped sentence, awkward pause, or poorly timed interruption can break conversational flow and erode user trust. At the same time, enterprises and startups alike need systems that can scale efficiently without piling on complexity.

Conversational Speech Recognition (CSR) provides the missing layer. By integrating conversation awareness directly into recognition, CSR allows developers to build agents that interact naturally and reliably without fragile chains of stitched-together components.

What’s Next

CSR is a new paradigm, and its time has come. Developers are already pushing the limits of ASR in conversational settings, and the demand for more natural, responsive agents will only accelerate.

With the launch of Flux, the first model purpose-built for Conversational Speech Recognition, we are putting this vision into practice. Flux shows how embedding turn-taking intelligence directly into recognition makes it possible to build voice agents that listen, think, and respond more like humans.

CSR is the foundation for the next generation of voice AI, and Flux is only the beginning.