Text Transcription in Labvanced
Text transcription involves converting spoken language into written text. By transcribing text, the verbal contents become searchable, readable, and analyzable. The primary goal of transcription is to make the content of verbal responses accessible for review, analysis, and documentation.
Table of Contents
Transcription Technology
Supporting Integration - Whisper
The transcription automatically occurs in Labvanced via a helpful Whisper integration. Whisper is a pre-trained model used for automatic speech recognition (ASR) that has been trained on 680K hours of labelled data.
Client-side Privacy
As a result of this integration, all processing and transcription occurs solely on the participant’s device. Client-side processing ensures no personal data like voice data is transmitted to third-party servers.
Use Cases of Text Transcription in Labvanced
1. Verbal Response Transcription
This use case employs transcription as a core method for data collection. When an experiment requires participants to provide spoken answers, the audio recording object captures their verbal responses. By integrating the relevant events for transcription, the spoken words are then automatically converted into written text. This resulting text can then be saved alongside other quantitative data from the experiment, ready for detailed post-session analysis.
In this demo, participants are asked to respond verbally to a series of questions while having the option to preview their responses before submitting. Once submitted, the answer is then transcribed on the screen for a preview of the text transcription feature.
2. Control the Experiment with Voice
Transcription can also be used to create a more interactive and accessible experimental environment by allowing participants to control the experiment with their voice. In this setup, specific spoken words or phrases can be specified to act as commands. When the participant speaks, the audio is transcribed into text in real-time. Labvanced then recognizes this text as a command to trigger a specific action. For example, in a task where a participant must categorize stimuli, they can simply say "True" or "False", or “Left” and “Right”, instead of having to press a key.
Categorize the presented image by saying out loud where it should go: LEFT or RIGHT. Press space bar while speaking to record your answer. Release the space bar to stop recording and submit your answer.
3. Real-Time Analysis and Dynamic Task Assignment
This use case takes the ideas from the previous applications and goes a step beyond by using transcription as the basis of real-time, adaptive experimental design. A participant’s verbal response is transcribed and immediately fed into an AI model, like ChatGPT, for instantaneous analysis. This analysis can perform complex tasks such as determining the emotional sentiment of the response, calculating the frequency of certain word types, or classifying the participant’s statement into one of several predefined psychological categories. Based on the output of this AI analysis, the experiment can then dynamically assign the next task, tailoring the experience to the individual's inferred state. For instance, if the AI detects a high level of anxiety in a response, the system could present a calming task next, creating a highly personalized and responsive research protocol.
Speech-to-Text and Verbal Response Transcription in Labvanced Experiments
Text transcription in Labvanced transforms spoken responses into structured, analyzable data, enabling researchers to seamlessly integrate verbal information into psychology and behavioral experiments. By combining a robust speech recognition system with fully client-side processing, Labvanced ensures both high transcription accuracy and strong data privacy.
Whether used for capturing verbal responses, enabling voice-controlled task interactions, or powering real-time adaptive experimental designs through AI-driven analysis, transcription expands the methodological toolkit available to researchers. This flexibility allows for richer data collection, more natural participant interaction, and increasingly personalized research protocols—without compromising usability or privacy.