Real-time Transcription allows users to follow along with spoken content in real time. AI Advances have made Real-time Transcription accessible and affordable, increasing its adoption across industries from education, entertainment, and media to legal.
What is Automated Real-time Transcription?
Automated Real-time Transcription refers to technology that uses artificial intelligence to transcribe spoken content to text in real time. As speakers talk, AI models generate text, then show it on a screen.
We speak three times faster than we write. Thus, human transcribers are not capable of real-time transcription. AI models had accuracy issues, but recent advances have brought them close to human accuracy.
How does AI-assisted Real-time Transcription work?
The Real-time Transcription process is not very different than pre-recorded (i.e., batch) transcription. The audio input comes from the live-streaming source, whether a microphone or an audio source. AI software used for real-time transcription, such as Cheetah, processes the audio and provides visual feedback, i.e., text, as it transcribes audio.
Use Cases for Real-Time Transcription
Real-time Transcription is a vital enabler of accessibility for people with hard of hearing or difficulty processing auditory information. Moreover, it has created new use cases or improved the existing ones across industries, such as meeting transcription, agent assistance, speech analytics, public speaking coaches, medical dictation, and inspection reporting.
3 Key Metrics for Evaluating Real-Time Transcription Engines
1. Accuracy:
Recent advances in AI have improved the accuracy of AI models. However, different Real-time Transcription software returns different results. Especially legacy ones do not have global models and cannot accurately transcribe different dialects and accents. Picovoice published an open-source framework to benchmark various transcription engines using WER.
2. Latency:
Real-time Transcription, as the name suggests, should happen in real time with no delay. Latency determines how fast a word appears after the speaker says the word. A higher latency causes a more noticeable mismatch between what humans hear and read. Given humans can detect half a millisecond delay, even a few hundred milliseconds, let alone a second, can ruin the experience. An unstable internet connection, the location of servers, or the network traffic can affect the latency.
Cloud-based Real-time Transcription engines always have latency compared to on-device Real-time Transcription due to the inherent limitations of cloud computing. Thus, time-sensitive applications should use on-device Real-time Transcription engines, such as Cheetah, instead of cloud-based ones. Only on-device Real-time Transcription engines can offer “real” real-time experience.
3. Reliability:
Latency shows delays in the service, whereas reliability focuses on service disruptions. Reliability measures the frequency of failures. It can happen due to internal or external factors, as in the recent Google Speech-to-Text outage. Vendors cannot eliminate it, as it’s also an inherent limitation of cloud computing. Enterprises cannot control it, carrying the vendor’s risk with a cloud-based Real-time Transcription. On the other hand, on-device speech processing gives control to enterprises, allowing them to manage their risks.
If you’re ready to use Real-time Transcription with high accuracy, no delay or reliability issues, start building now!
