Real-time transcription has become the backbone of conversational AI and modern voice automation. But building it effectively requires more than accurate speech recognition – it demands speed, reliability, and the right infrastructure.
In this blog, we explore how to do real-time transcription with low latency, breaking down the technical challenges and proven solutions. We’ll examine the role of streaming protocols, speech-to-text engines, and how top programmable voice AI APIs with low latency help developers create natural, responsive experiences.
Understanding the mechanics of a voice API for developers is key to moving from prototype to production.
Why Low-Latency Transcription Matters
Real-time transcription is no longer a luxury feature. For AI-driven products, customer support systems, or meeting assistants, it is the foundation of natural conversation. The goal is simple: when someone speaks, the words should appear or be acted upon almost instantly. In practice, that means a delay of just a few hundred milliseconds.
A delay of two seconds may not sound like much, but in live conversations it feels awkward. The speaker pauses, the listener waits, and the flow breaks. This is why product managers and engineering leads working with voice agents put as much emphasis on latency as they do on accuracy. A transcript that arrives late is almost as unusable as one that is wrong.
What Is Real-Time Transcription?
Real-time transcription is the process of capturing spoken words and converting them into text while the speaker is still talking. Instead of uploading a recording and waiting minutes for results, the system delivers continuous text streams.
There are two main output types:
- Interim transcripts: These appear quickly, often within a fraction of a second, but may change as more audio is processed.
- Final transcripts: These are confirmed and stable, though they arrive with a slight delay.
Both types are important. Interim transcripts keep the user engaged, while final transcripts guarantee accuracy.
How Does Real-Time Transcription Work Technically?
Every real-time transcription pipeline, regardless of technology or vendor, follows a similar sequence:
- Audio Capture – Microphone input, VoIP stream, or telephony call is recorded in small frames (for example, 20 milliseconds each).
- Transport – These frames are sent over low-latency protocols such as WebSockets, WebRTC, or RTP in telephony networks.
- Speech-to-Text (STT) Processing – The STT engine receives the frames, runs inference, and produces text tokens.
- Post-Processing – Optional steps add punctuation, capitalization, or redact sensitive data.
- Downstream Applications – The text can trigger logic in a chatbot, large language model, or tool.
- Output – The result may be displayed as live captions or passed to text-to-speech to continue the conversation.
This chain runs continuously so that words flow in near real-time.
What Causes Latency in Real-Time Transcription?
Even the fastest systems cannot avoid some delay. The key is to understand where it comes from and how much each stage contributes.
- Audio capture and buffering: Each audio frame is a small slice of time, usually 20 to 40 milliseconds. Buffers add a little more delay to smooth out network jitter.
- Network transmission: Sending data across regions introduces round-trip times. Within the same region, this might be 20–60 ms. Crossing regions adds 50–100 ms for each hop. Intercontinental routing can add 200 ms or more.
- STT inference: The speech-to-text engine is usually the largest contributor. Generating the first tokens can take 150–300 ms. In many systems, this stage accounts for more than half of total latency.
- Post-processing: Adding punctuation, diarization, or redaction may add another 50–100 ms.
- Downstream processing: If the text is passed to a language model or a text-to-speech engine, that adds another 150 to 300 ms before a spoken reply begins.
When you add these up, a well-optimized pipeline can deliver under 400 ms. A poorly tuned one can easily exceed a second, which feels slow in a live conversation. Industry telecom standards accept that one-way latency of 150 ms or less is generally imperceptible in real-time voice applications.
How Can You Reduce Latency in Real-Time Transcription?
There are practical steps to reduce delay without sacrificing too much accuracy.
Optimize Capture
- Keep audio frames small, ideally 20 ms.
- Use codecs designed for low delay, such as PCM or Opus.
- Avoid stacking too many audio filters if the STT engine can handle noisy input.
Reduce Network Overhead
- Run STT engines close to where audio is captured.
- Keep calls and transcription within the same region when possible.
- Use private network paths if available to avoid unpredictable internet hops.
Use Streaming STT
- Choose APIs that support streaming rather than batch processing.
- Send audio in small frames and receive interim transcripts immediately.
- Do not wait for entire sentences; process as soon as partial text is available.
Tune Endpointing
- Endpointing decides when the speaker has stopped talking.
- Aggressive endpointing gives faster responses but risks cutting speech short.
- Conservative endpointing waits longer and increases accuracy.
- A balanced approach is to show interim results quickly but trigger actions only when final text arrives.
Overlap Pipelines
- Stream transcripts into downstream systems without waiting for a full sentence.
- Let the language model start processing while the speaker is still talking.
- Begin text-to-speech playback as soon as the first tokens are ready.
By treating the system as a continuous stream instead of sequential steps, you keep interactions fluid and natural.
What Protocols and Architectures Are Best for Low-Latency STT?
The transport layer plays a big role in how quickly transcription can happen. Different contexts use different approaches.
WebSockets
A common choice for developers. Audio frames are sent to the server, and transcription events are received back in the same connection. This is straightforward to implement and widely supported.
WebRTC
Designed for browser and mobile environments. It has built-in features like jitter buffering, echo cancellation, and adaptive bitrate. It is ideal for conferencing or live collaboration where many participants need low-latency transcription.
SIP/RTP
The standard in telephony and VoIP systems. Audio flows as RTP packets, which can then be bridged into a WebSocket-based STT service using a media gateway. This is essential for contact center and call automation use cases.
Speed Versus Accuracy
In every project, you must decide how much speed you are willing to trade for accuracy.
- Interactive assistants or IVR systems: These need responses within 200–300 ms. Even if accuracy is slightly lower, the fast response keeps the user engaged.
- Captions and subtitles: A delay of up to 500 ms is acceptable. Stability matters more than raw speed.
- Medical, financial, or legal transcripts: Accuracy is critical, and users are willing to accept longer delays. These often require additional processing such as redaction or speaker separation.
The best practice is to use interim transcripts to maintain the feeling of immediacy and then confirm with final transcripts for reliability. Modern ASR systems, employing techniques like SpecAugment, can achieve WERs of 5–7 % even in noisy or challenging speech contexts.
Discover how to integrate AI-powered voice APIs into traditional IVR systems to modernize call handling and reduce customer friction.
Building a Simple Streaming Pipeline
To understand how the pieces fit together, consider a minimal design:
- Capture audio from microphone or telephony input.
- Encode into 16 kHz PCM frames of 20 ms each.
- Send frames to the STT service via WebSocket.
- Receive interim transcripts continuously, and display them as live captions.
- Receive final transcripts after short pauses, and pass them to downstream logic such as a chatbot.
- If needed, feed responses into text-to-speech and play them back to the user.
This design ensures that text appears quickly and actions are taken only when the system is confident.
Where Does FreJun Teler Fit in the Stack?
Building real-time transcription pipelines is rarely as simple as capture, STT, and output. In reality, teams must stitch together telephony systems, VoIP, streaming protocols, and AI models – and the biggest challenge is moving live audio reliably with minimal delay.
FreJun Teler solves this by acting as the foundation voice layer. It bridges traditional telephony (SIP, PSTN) and modern channels (WebRTC, VoIP) directly to your AI stack, while handling global media transport, jitter buffering, and packet loss recovery in the background. Unlike platforms tied to specific AI models, Teler is fully model-agnostic, letting you connect any speech-to-text, language model, or text-to-speech system.
With developer-first SDKs and events for transcripts, barge-in, and call control, Teler lets you focus on designing intelligent product logic while it manages the complexity of voice infrastructure at scale.
Step-by-Step building a Low-Latency Voice Agent with Teler
Let’s walk through what a practical build looks like when you combine Teler with your preferred AI components.
Step 1: Capture Audio
- A customer calls a business number or speaks into a web app.
- Teler captures the audio in 20 ms frames, keeping jitter buffers tight.
Step 2: Stream to STT
- Teler forwards the frames via WebSocket to your chosen speech-to-text API.
- The STT begins returning interim transcripts within 200–300 ms.
Step 3: Process with LLM
- Interim transcripts can feed live captions.
- Final transcripts are passed to your large language model (for example GPT, Claude, or LLaMA) which decides the next action.
Step 4: Add RAG or Tools
- The LLM can query a knowledge base (retrieval augmented generation).
- It can also trigger external tools, such as CRMs or booking systems.
Step 5: Generate Response with TTS
- The LLM’s text reply is streamed into a text-to-speech service.
- Audio is sent back through Teler to the caller or user.
This end-to-end loop creates a fluid voice agent. Latency is kept low because each stage is streaming rather than waiting for the full sentence.
Learn how Retrieval Augmented Generation (RAG) boosts accuracy in voice agents and ensures conversations stay contextually precise every time.
Reducing Latency Further: Practical Tips
When integrating multiple APIs, every stage introduces some overhead. To keep end-to-end latency low:
- Choose regional servers close to your users. If your customer is in Europe but your STT runs in the US, you immediately add 150 ms or more.
- Stream everything. From STT to LLM to TTS, avoid blocking calls. Use partial tokens as soon as they are available.
- Overlap stages. Start LLM inference while STT is still processing. Start TTS playback while LLM is still generating tokens.
- Handle barge-in. Allow users to interrupt TTS playback so that conversations feel natural.
- Measure continuously. Track latency at each stage: ingress, STT, LLM, TTS, and playback.
Testing and Monitoring in Production
A system that works in a demo can fail at scale if not tested carefully.
- Load testing: Simulate hundreds of concurrent calls to test throughput.
- Latency testing: Measure p95 and p99 latency, not just averages. Users experience the worst-case delays.
- Golden path testing: Validate with different accents, noisy backgrounds, and varied speaking speeds.
- Monitoring: Collect metrics for each stage and build dashboards. For example, how long does STT take to produce the first interim token? How long before TTS starts playback?
- Alerts: Set thresholds. For example, alert if latency exceeds 600 ms in a given region.
Compliance and Security Considerations
Low latency is only part of the story. For enterprise use cases, compliance and data protection are equally important.
- Encryption: Ensure all audio streams are encrypted in transit.
- Data residency: For regulated industries, keep transcription servers within specific regions.
- Redaction: Automatically remove sensitive data such as card numbers or personal identifiers before storing transcripts.
- Access control: Only authorized services should access transcripts and audio streams.
- Retention policies: Store transcripts only as long as necessary for business or compliance needs.
Teler is designed with these principles built in so you don’t have to reinvent them for each deployment.
Use Cases That Benefit From Low-Latency Transcription
Different industries have different latency expectations, but all gain value from real-time transcription.
- Inbound Call Handling: A virtual receptionist that can greet callers, answer questions, and route calls effectively only works if speech is transcribed in near real-time. Even a one-second lag creates frustration.
- Outbound Campaigns: Appointment reminders, feedback surveys, and qualification calls can be automated. Low latency ensures the call feels like a natural conversation instead of a machine reading a script.
- Meetings and Collaboration: Live captions, meeting notes, and AI assistants rely on accurate interim transcripts. A system that lags makes meetings harder instead of easier.
- Healthcare and Telemedicine: Doctors can dictate notes during consultations. Latency here affects productivity and the patient’s perception of technology.
- Financial Services: Compliance transcription requires accuracy, but customer-facing interactions still need speed. Balancing the two is critical.
Scaling Globally
When moving from prototype to production, latency challenges become more complex at scale.
- Geo-distribution: Deploy transcription servers or choose vendors with multiple regional points of presence.
- Failover: Calls should reroute automatically if one region has issues.
- Traffic spikes: Plan for sudden increases, such as a campaign generating thousands of concurrent calls.
- Observability at scale: Aggregate metrics across regions and time zones.
Scaling is not only about handling more calls but about keeping the same latency guarantees everywhere.
The ROI of Getting Latency Right
Founders and product managers often ask why latency is worth such focus. The answer is simple: low latency improves adoption and retention.
- Better customer experience: Users are more likely to use and trust systems that feel responsive.
- Higher conversion: Outbound campaigns with natural conversation convert better.
- Increased efficiency: Agents can handle more calls when automation is smooth.
- Compliance advantage: Real-time redaction and monitoring reduce risk exposure.
Latency is not just a technical metric. It is a direct business lever.
Conclusion
Real-time transcription is the foundation of next-generation voice automation. The challenge is not only converting speech into text but doing it quickly enough to preserve a natural, conversational flow. Achieving this requires careful control over every stage – from audio capture and network routing to transcription, processing, and playback. Building such an infrastructure internally is complex, costly, and time-consuming.
FreJun Teler solves this by serving as the dependable voice layer beneath your AI stack. It allows you to integrate any STT, LLM, or TTS engine while ensuring low-latency, enterprise-grade voice transport.
Ready to take the next step?
Schedule a demo with FreJun Teler and see how quickly you can bring real-time, low-latency transcription to life.
FAQs –
Q1. Why is low latency critical in real-time transcription?
Low latency keeps conversations natural, prevents awkward pauses, and ensures voice agents respond instantly without breaking conversational flow.
Q2. Can I use different STT, LLM, and TTS providers with one voice API?
Yes, a model-agnostic voice API lets you integrate any STT, LLM, or TTS while handling transport, latency, and scaling.
Q3. How does streaming differ from batch transcription?
Streaming delivers continuous partial and final transcripts in real-time, while batch waits for entire recordings before processing.
Q4. What’s the best latency target for production-ready voice agents?
For interactive agents aim under 300 ms, for captions under 500 ms, balancing responsiveness with transcription accuracy.