When you speak into a transcription tool, you might expect the text to appear instantly. But there's always a gap between your words and the transcript. This delay, called latency, ranges from a few hundred milliseconds to several seconds depending on your setup.
Understanding what causes latency helps you choose the right transcription approach for your needs, whether you're running live captions, building a voice assistant, or simply transcribing recorded meetings.
What Is Speech-to-Text Latency?
Latency in speech-to-text refers to the total time between when you speak a word and when you see it as text. For cloud-based systems, this typically ranges from 500 to 1,200 milliseconds. The fastest commercial providers achieve 200-300ms, while slower systems can take 2-3 seconds or more.
This might sound like a small difference, but it matters. Human conversation operates within a 300-500 millisecond response window. Delays beyond 500ms feel unnatural and frustrating, especially in interactive applications like voice assistants or live captioning.
The latency you experience isn't a single delay, but a chain of steps:
- Audio capture - Your microphone records and digitizes sound (20-100ms)
- Transmission - Audio travels to the processing server (variable)
- Buffering - The system collects enough audio to process (20-250ms)
- Model inference - Neural networks analyze the audio (100-500ms)
- Post-processing - Adding punctuation and formatting (20-50ms)
- Return transmission - Text travels back to your device (variable)
Each step adds delay. The total depends on your network conditions, the provider's infrastructure, and the complexity of processing features you've enabled.
Why Can't Transcription Be Instant?
Several technical constraints make instant transcription impossible with current technology.
Neural Networks Need Context
Modern speech recognition uses deep learning models that analyze patterns across time. Unlike simple audio-to-letter mapping, these systems consider surrounding sounds to make accurate predictions. An isolated syllable could be many different words. The model waits for more context before committing to a transcription.
This is why you often see partial results that change as you continue speaking. The system refines its predictions as it receives more information.
Cloud Processing Adds Network Overhead
Most speech-to-text services run in the cloud because the neural network models are too large and computationally intensive for typical devices. Your audio must travel to remote servers, get processed, and return as text. Physical distance and network congestion add unavoidable delay.
A user in New York connecting to servers in Virginia experiences less latency than someone connecting from Singapore. Network jitter can cause additional variability, making some words appear faster than others.
Real-Time vs. Batch Trade-offs
Real-time streaming transcription prioritizes speed over accuracy. The system emits words as soon as it's reasonably confident, sometimes correcting itself later. Batch processing waits until the entire audio file is available, allowing more sophisticated analysis and higher accuracy, but with much longer overall latency.
Voice assistants need sub-300ms latency to feel responsive. Podcast transcription can tolerate minutes of processing time if it means fewer errors.
Typical Latency by Provider
Performance varies significantly across speech-to-text services. Based on recent benchmarks:
- Google Cloud Speech-to-Text: 200-250ms (among the fastest)
- Deepgram Nova-3: Sub-300ms latency
- AssemblyAI: 300-600ms for streaming
- Amazon Transcribe: 2-3 seconds (significantly slower)
These numbers represent ideal conditions. Real-world performance depends on your location, network quality, and the specific features you enable. Adding real-time redaction, for example, can add 50-300ms of additional processing time.
How to Minimize Latency
If transcription speed matters for your use case, several strategies can help reduce delays.
Choose the Right Connection Method
WebSocket connections maintain a persistent channel between your application and the transcription service. This eliminates the connection setup overhead that REST APIs require, saving 50-100ms per request. For conversational applications with multiple back-and-forth exchanges, WebSockets can save seconds of cumulative delay.
Optimize Your Audio Buffer
The buffer size determines how much audio you send in each streaming message. Too large and you build in unnecessary delay. Too small and you create network overhead. Most providers recommend buffers between 20-250ms, with 100ms often being the sweet spot.
Consider On-Device Processing
Running transcription locally eliminates network latency entirely. Apple's Siri and Google's offline voice typing use on-device models for basic commands. The trade-off is reduced accuracy, especially for complex vocabulary or challenging audio conditions. On-device models are also constrained by your hardware's processing power.
Reduce Post-Processing Features
Features like punctuation, capitalization, and speaker diarization require additional processing. If you need the fastest possible results, consider requesting raw transcripts and adding formatting client-side where you have more control over the trade-offs.
When Latency Matters (And When It Doesn't)
Not every transcription task requires minimal latency. Matching your requirements to the right approach saves cost and complexity.
Low latency critical:
- Voice assistants and conversational AI
- Live captioning for broadcasts
- Real-time translation
- Accessibility accommodations
Latency flexible:
- Meeting transcription after the fact
- Podcast and video post-production
- Research interview analysis
- Content repurposing workflows
For recorded content, batch processing typically delivers better accuracy at lower cost per minute. The audio isn't going anywhere, so there's no benefit to rushing the transcription.
Making the Right Choice
Understanding latency helps you set realistic expectations and choose tools that match your workflow. If you're transcribing recorded meetings or podcasts, a tool optimized for accuracy makes more sense than one built for real-time voice agents.
Scriby focuses on transcription quality for recorded audio and video files. Rather than chasing the fastest possible streaming speeds, it prioritizes accurate results with speaker identification, giving you clean transcripts you can actually use. You upload your file, and the processing happens in the background while you work on other things.
For most professionals working with recorded content, the few minutes of processing time matters far less than getting a transcript that doesn't require extensive editing.