AI-Driven Web-Based Speech Transcription Tool: A Novel Approach for Efficient Evaluation of Verbal Memory Performance

Memory deficits are prevalent in epilepsy and other brain disorders, significantly affecting quality of life. In particular, patients with mesial temporal lobe epilepsy require ongoing monitoring and repeated memory assessments to track cognitive function, which could benefit from automated transcription tools. We used a classic free recall (FR) verbal memory task, where participants recalled words, to assess their performance by recording, transcribing, and detecting vocalizations of correctly remembered words. Conventional manual speech transcription methods are time-consuming and prone to human error, especially in a noisy hospital environment. To address these limitations, a modified U-Net architecture was employed for noise reduction, resulting in a signal-to-noise ratio (SNR) of 15.8 and a mean squared error (MSE) of 0.0021. We also developed an automated transcription interface utilizing the Whisper speech recognition model, which was fine-tuned for Polish, Czech, and English languages. Dynamic Time Warping (DTW) was applied to provide precise word-level timestamps of vocalization onset and offset. The interface was iteratively refined over eight development cycles, incorporating feedback from target users. Transcription accuracy was evaluated with Word Error Rates (WER) of 10.3% for Czech, 7.1% for Polish, and 5% for English, alongside respective Character Error Rates (CER) of 12%, 10.8%, and 7.5%. Our automated interface outperformed manual transcription, reducing transcription time fourfold and achieving 87.5% user satisfaction. These results demonstrate robust transcription accuracy of the challenging Slavic languages and highlight the potential of automated transcription to streamline speech processing using emerging human-computer interface technologies.