What is Real-time Transcription?

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Customer service isn’t just about solving problems anymore, canada phone number list it’s about creating seamless, connected experiences from the very first interaction. With ServiceNow CRM, organizations are already bridging the gap between departments, unifying customer data, and automating service workflows. But as customer expectations continue to evolve, so does the need for tools that enhance both speed and accuracy in live interactions.

That’s where Real-Time Transcription (RTT) comes in. When integrated with ServiceNow CRM, RTT captures and transcribes conversations as they happen, providing agents instant access to dialogue, reducing manual effort, and enabling smarter, faster support. So what exactly is RTT, and how is it transforming the way service teams operate within ServiceNow?




Real-time transcription is the live conversion of spoken words into written text as a conversation occurs. Using advanced speech recognition technology, it processes voice inputs, such as those during a phone call between a customer and agent, and displays the transcribed text within seconds, often with near-instant accuracy. The technology enables features including live captions, automated note-taking, and searchable conversation records, making it easier to follow discussions, retain information, and support accessibility in real time.



What are the key components of Real-time Transcription?

1. Audio Capture
2. Automatic Speech Recognition (ASR) Engine
3. Large Language Model (LLM)
4. Text Formatting and Punctuation
5. Speaker Diarization

1. Audio Capture
This is the starting point. Real-time transcription begins with capturing spoken words through microphones, headsets, or telephony systems. The quality of the audio input, including clarity, volume, and background noise, has a significant impact on the transcription’s accuracy. In voice calls, this often involves capturing both the customer and agent audio streams simultaneously.

2. Automatic Speech Recognition (ASR) Engine
At the heart of real-time transcription lies the ASR engine, which listens to the audio and translates it into text. It uses deep learning models trained on massive datasets of speech and language to recognize words and phrases. ASR continuously processes incoming audio in real time, making near-instant decisions about what’s being said.

3. Large Language Model (LLM)
Large Language Models help the ASR engine understand context and grammar. For example, it helps determine whether the speaker said "there" or "their" based on sentence structure. These models improve the fluency and coherence of the transcription, especially in industry-specific conversations that may involve jargon or technical terms.

4. Text Formatting and Punctuation
To make transcripts easy to read and useful in real time, transcription systems include automatic formatting features. This involves inserting punctuation, capitalizing proper nouns, and breaking text into readable segments. These refinements happen on the fly, enhancing readability and making it easier for agents and supervisors to follow along.

5. Speaker Diarization
In conversations with multiple participants, such as a voice call between agent and customer, speaker diarization identifies and distinguishes who is speaking. This is critical for customer service environments, where understanding who said what can impact how cases are documented, analyzed, or escalated.